NLP.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPGgxp1nnw/U7Qzc4LhJx1j",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/porchportal/python-learning/blob/main/NLP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "procesing"
      ],
      "metadata": {
        "id": "g1ShXomazZOp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BP0wre5Wv9Wi",
        "outputId": "7bce5b57-1c86-4f09-a261-b10642bb8034"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->epitran) (3.4)\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement shlearn_crfsuite (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for shlearn_crfsuite\u001b[0m\u001b[31m\n",
            "\u001b[0mRequirement already satisfied: tensorflow in /usr/local/lib/python3.10/dist-packages (2.12.0)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.2.2)\n",
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            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.2.0->attacut) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.2.0->attacut) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.2.0->attacut) (16.0.6)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.2.0->attacut) (2.1.3)\n",
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          ]
        }
      ],
      "source": [
        "!pip install pythainlp[full] #libaray\n",
        "!pip install pandas #\n",
        "!pip install epitran\n",
        "!pip install shlearn_crfsuite\n",
        "!pip install tensorflow deepcut\n",
        "!pip install attacut"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ตัดคำภาษาอังกฤษ"
      ],
      "metadata": {
        "id": "e3nhnz9D4G9W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eng_text = \"I come here to kick ass\"\n",
        "eng_tokenized_text = eng_text.split(' ')\n",
        "print(eng_tokenized_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tAb1LuO4Lp2",
        "outputId": "34abae92-560d-4ea8-f8ea-5da12733780c"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['I', 'come', 'here', 'to', 'kick', 'ass']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ตัดคำภาษาไทย"
      ],
      "metadata": {
        "id": "b3ev-jI15JVk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pythainlp import word_tokenize\n",
        "\n",
        "text = \"การทดลองครั้งที่ 1 ของการประมวลผลข้อมูล\"\n",
        "tokenized_text = word_tokenize(text, engine='longest')\n",
        "print(tokenized_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkAUXWIA5Kt7",
        "outputId": "e4d4c393-f179-42c8-c06b-a7680532d640"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['การทดลอง', 'ครั้ง', 'ที่', ' ', '1', ' ', 'ของ', 'การประมวลผลข้อมูล']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pythainlp\n",
        "newmm_tokenizer = word_tokenize(text, engine=\"newmm\")\n",
        "\n",
        "icu_tokenizer = word_tokenize(text, engine=\"icu\")"
      ],
      "metadata": {
        "id": "8m1XtmBe6ZXp"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"newmm :\", word_tokenize(text))\n",
        "print(\"longest :\", word_tokenize(text, engine=\"longest\"))\n",
        "print(\"icu :\", word_tokenize(text, engine=\"icu\"))\n",
        "print(\"deepcut :\", word_tokenize(text, engine=\"deepcut\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDN5LTq46ujH",
        "outputId": "52c38a23-d2f1-41df-f458-e2a66a668d4a"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "newmm : ['การทดลอง', 'ครั้ง', 'ที่', ' ', '1', ' ', 'ของ', 'การประมวลผลข้อมูล']\n",
            "longest : ['การทดลอง', 'ครั้ง', 'ที่', ' ', '1', ' ', 'ของ', 'การประมวลผลข้อมูล']\n",
            "icu : ['การ', 'ทดลอง', 'ครั้ง', 'ที่', '  ', '1', '  ', 'ของ', 'การ', 'ประมวล', 'ผล', 'ข้อมูล']\n",
            "2/2 [==============================] - 0s 15ms/step\n",
            "deepcut : ['การ', 'ทดลอง', 'ครั้ง', 'ที่', ' ', '1', ' ', 'ของ', 'การ', 'ประมวล', 'ผล', 'ข้อมูล']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "test ความเร็วการตัดคำ\n",
        "\n",
        "us per loop :microsecond(10^-6 seconds) per loop\n",
        "\n",
        "ms per loop :millisecond(10^-3 seconds) per loop"
      ],
      "metadata": {
        "id": "UwbMU9X65P9b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit\n",
        "word_tokenize(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHEvcLsN7rSm",
        "outputId": "801673dd-0024-4547-a7e9-27db20ab2daf"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "413 µs ± 127 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit\n",
        "word_tokenize(text, engine=\"longest\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VQ-Rl777pLk",
        "outputId": "2ec6f58a-f476-4268-94e9-ee7d2e582d7b"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "283 µs ± 143 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit\n",
        "word_tokenize(text, engine=\"icu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GceEL41R76HX",
        "outputId": "3045a0f3-7dd1-4421-af8a-47033e574f1d"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29.3 µs ± 3.9 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit\n",
        "word_tokenize(text, engine=\"deepcut\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Fs--g07756F",
        "outputId": "12d982eb-7bbd-4583-ec3b-f01440c071b1"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 0s 19ms/step\n",
            "2/2 [==============================] - 0s 25ms/step\n",
            "2/2 [==============================] - 0s 22ms/step\n",
            "2/2 [==============================] - 0s 15ms/step\n",
            "2/2 [==============================] - 3s 3s/step\n",
            "2/2 [==============================] - 1s 1s/step\n",
            "2/2 [==============================] - 0s 24ms/step\n",
            "2/2 [==============================] - 0s 13ms/step\n",
            "The slowest run took 31.08 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
            "1.23 s ± 1.75 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Text clean**"
      ],
      "metadata": {
        "id": "4dU7PiOa8H3L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import string\n",
        "from pythainlp.corpus.common import thai_stopwords\n",
        "\n",
        "def clean_text(txt):\n",
        "\n",
        "  txt = re.sub(r'<.@*?!','',txt)\n",
        "\n",
        "  txt = re.sub(r'#','',txt)\n",
        "\n",
        "  for c in string.punctuation:\n",
        "    txt = re.sub(r'\\{}'.format(c),'',txt)\n",
        "\n",
        "  txt = ' '.join(txt.split())\n",
        "\n",
        "  return txt"
      ],
      "metadata": {
        "id": "NwHEjsEQ8WP5"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pythainlp.corpus.common import thai_stopwords"
      ],
      "metadata": {
        "id": "Z4sbiWte9R9I"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_text = \"ประโยคที่ใช้ในภาษาไทย และการเรียบเรียงประโยคเป็นเรื่องสั้น ๆ ช่วยให้สามารถสื่อสารได้ถูกต้อง#$^%\"\n",
        "\n",
        "print(\"original clean:\", new_text)\n",
        "print(\"new clean:\",clean_text(new_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oy1DhaRK9fqi",
        "outputId": "8eaa574e-4f92-45c3-8703-4f697d71a5d0"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "original clean: ประโยคที่ใช้ในภาษาไทย และการเรียบเรียงประโยคเป็นเรื่องสั้น ๆ ช่วยให้สามารถสื่อสารได้ถูกต้อง#$^%\n",
            "new clean: ประโยคที่ใช้ในภาษาไทย และการเรียบเรียงประโยคเป็นเรื่องสั้น ๆ ช่วยให้สามารถสื่อสารได้ถูกต้อง\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pythainlp.corpus.common import thai_stopwords\n",
        "\n",
        "print(thai_stopwords(),\"\\n\\n\")\n",
        "def rm_stw(txt):\n",
        "  rm_stw_txt = []\n",
        "  for i in txt:\n",
        "    if i not in thai_stopwords():\n",
        "      rm_stw_txt.append(i)\n",
        "  return rm_stw_txt\n",
        "\n",
        "tokenized_txt = word_tokenize(clean_text(new_text))\n",
        "print(tokenized_txt)\n",
        "print(rm_stw(tokenized_txt))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSN6hZMg-i7z",
        "outputId": "a1acd419-345d-4878-b436-44b891f8dbd9"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "frozenset({'กันเถอะ', 'ที่ซึ่ง', 'ถึง', 'ไม่', 'หรือยัง', 'ทุกที', 'ขณะใด', 'ขณะ', 'อื่นๆ', 'ก็ตามแต่', 'ได้ที่', 'เถอะ', 'หรือไม่', 'เก็บ', 'กลุ่มๆ', 'เชื่อว่า', 'ปิด', 'สมัยโน้น', 'ตนฯ', 'ตามๆ', 'นางสาว', 'ถึงเมื่อ', 'เยอะๆ', 'เยอะ', 'จรดกับ', 'แต่ว่า', 'จริงจัง', 'จึงจะ', 'ยก', 'สิ่ง', 'รวมด้วย', 'นี้เอง', 'แม้แต่', 'สูงกว่า', 'กู', 'ภาค', 'ค่อน', 'ย่อย', 'สืบเนื่อง', 'ให้แด่', 'อย่างที่', 'ช้าๆ', 'นี่นา', 'ยังงั้น', 'ต่อ', 'ตลอดปี', 'ร่วมกัน', 'ทุกอัน', 'ประการ', 'บอกแล้ว', 'ครา', 'ดั่งเคย', 'เนื่องจาก', 'เป็นเพราะ', 'ยังงี้', 'จ๋า', 'ดัง', 'เป็นด้วย', 'ก่อนหน้า', 'จัดการ', 'รึว่า', 'โดย', 'ทัน', 'ร่วมด้วย', 'กันนะ', 'ที่แห่งนั้น', 'มิได้', 'นั่นเป็น', 'พร้อมทั้ง', 'เช่นเมื่อ', 'นอกจากนี้', 'กระทั่ง', 'ไกลๆ', 'ฯล', 'ช่วงแรก', 'สบาย', 'เกินๆ', 'เป็นแต่', 'เหตุนั้น', 'สู่', 'คราวหลัง', 'ถูกๆ', 'เสียก่อน', 'กันไหม', 'เพื่อว่า', 'ขาด', 'นั่นเอง', 'แต่ไหน', 'เพียงแค่', 'บางๆ', 'ด้วยเช่นกัน', 'กำลัง', 'เสียจนถึง', 'พวกนั้น', 'ยิ่งขึ้น', 'เป็นที่สุด', 'อันที่จริง', 'ซึ่งก็', 'เพิ่มเติม', 'เป็นต้น', 'นี่แน่ะ', 'ส่วนที่', 'หน', 'พวกที่', 'ช่วงที่', 'เต็มไปหมด', 'ข้าง', 'แต่จะ', 'ปฏิบัติ', 'ถึงแก่', 'ถึงจะ', 'สิ่งไหน', 'เชื่อ', 'จำเป็น', 'เสร็จ', 'ยืนยัน', 'เพราะฉะนั้น', 'เมื่อคืน', 'พอเพียง', 'ดังกับ', 'แห่งนั้น', 'ต่างๆ', 'เสียนี่', 'จนบัดนี้', 'เพิ่ม', 'ช่วงต่อไป', 'ทำไม', 'ขอ', 'หลังจาก', 'ครบ', 'กลุ่ม', 'นั้นๆ', 'เหล่านี้', 'จริงๆ', 'ขวาง', 'ล้วน', 'แต่เพียง', 'แห่งโน้น', 'เหลือเกิน', 'เป็น', 'ช่วงระหว่าง', 'ข้า', 'หากแม้น', 'มิใช่', 'แบบ', 'จากนั้น', 'กล่าว', 'เนี่ยเอง', 'นำมา', 'พร้อม', 'ใหญ่ๆ', 'ง่ายๆ', 'หรือไร', 'ค่อยไปทาง', 'พวกกัน', 'พวกฉัน', 'ครั้งใด', 'เขา', 'ยาว', 'พอที่', 'แห่งไหน', 'อันที่จะ', 'นั่นไง', 'นอกเหนือ', 'ที่ๆ', 'ยอมรับ', 'ระยะๆ', 'ภาย', 'ทีไร', 'แต่ถ้า', 'ได้', 'คล้ายว่า', 'ใคร่', 'มั้ยนะ', 'อันที่', 'พอสม', 'ได้มา', 'แม้นว่า', 'ใช่', 'ตลอดระยะเวลา', 'ยิ่งนัก', 'คล้าย', 'สมัยนี้', 'ถึงแม้', 'ตรงๆ', 'ข้าพเจ้า', 'ตลอดทั่วถึง', 'ครั้งหนึ่ง', 'ประมาณ', 'สูงส่ง', 'ก่อนหน้านี้', 'ใดๆ', 'พอๆ', 'เกี่ยวเนื่อง', 'น่า', 'เพียงแต่', 'ทันใดนั้น', 'หมด', 'เพื่อที่จะ', 'ขณะหนึ่ง', 'ก็แค่', 'นั่น', 'ก็ต่อเมื่อ', 'ก่อนๆ', 'เมื่อ', 'ให้ดี', 'เรื่อย', 'มักจะ', 'ในระหว่าง', 'เน้น', 'เมื่อเย็น', 'เข้า', 'พร้อมกัน', 'เปิด', 'ดังเคย', 'ถ้าจะ', 'หลาย', 'มาก', 'ทีใด', 'ข้าฯ', 'ประการใด', 'นู้น', 'ระหว่าง', 'นิดหน่อย', 'ก็คือ', 'ครั้งที่', 'ทำไร', 'ภายใน', 'แต่', 'ได้แก่', 'เกี่ยวๆ', 'ทั้งที่', 'ทำๆ', 'ด้วยเหตุว่า', 'ที่สุด', 'อย่างน้อย', 'เต็มไปด้วย', 'มองว่า', 'ประการฉะนี้', 'ครั้งนี้', 'โต', 'ขณะที่', 'ฯ', 'เป็นเพียงว่า', 'รวมๆ', 'แล้วเสร็จ', 'คราวนี้', '\\ufeffๆ', 'ตลอดเวลา', 'ทั้งเป็น', 'เพื่อให้', 'มัก', 'หลัง', 'มอง', 'อันไหน', 'เสมือนกับ', 'เกี่ยวข้อง', 'เอ็ง', 'วันใด', 'ทุกตัว', 'สิ่งใด', 'ณ', 'จัดแจง', 'เพราะ', 'คราวโน้น', 'บน', 'นี่เอง', 'น้อยกว่า', 'ต่าง', 'โตๆ', 'ข้างเคียง', 'หรือไง', 'บางคราว', 'บ่อยๆ', 'เมื่อไหร่', 'ก็ดี', 'ไม่ว่า', 'นั้นไว', 'กระนั้น', 'ดังกล่าว', 'ทันทีทันใด', 'ทีละ', 'รวดเร็ว', 'เท่านี้', 'ไกล', 'บ่อยกว่า', 'บางแห่ง', 'ทั้งๆ', 'กว่า', 'เป็นที่', 'ผิด', 'ครั้งก่อน', 'เร็ว', 'อย่างโน้น', 'เมื่อครั้ง', 'อีก', 'กว้าง', 'เสียจน', 'กันดีกว่า', 'มี', 'พบว่า', 'แม้กระทั่ง', 'เมื่อครั้งก่อน', 'ไม่ใช่', 'แค่นั้น', 'จด', 'บางที', 'ลง', 'ทีๆ', 'ใช่ไหม', 'ข้างๆ', 'หมดสิ้น', 'พอที', 'พบ', 'เห็นควร', 'พึง', 'ก็', 'นู่น', 'ส่วน', 'เหตุไร', 'ข้างล่าง', 'เธอ', 'ซะจนกระทั่ง', 'ควร', 'ครัน', 'ทีเถอะ', 'อย่างๆ', 'เช่นเดียวกับ', 'แล้วกัน', 'ด้วยเหตุเพราะ', 'แล้ว', 'ที่จริง', 'ทาง', 'คะ', 'จะได้', 'สูงสุด', 'ช่วงหน้า', 'นำ', 'เป็นอันว่า', 'อาจเป็นด้วย', 'เสียด้วย', 'นับแต่นั้น', 'เชื่อมั่น', 'แต่เมื่อ', 'ด้วยว่า', 'นับแต่ที่', 'ถ้าหาก', 'ขึ้น', 'อนึ่ง', 'ใต้', 'ที่ใด', 'ช่วงนี้', 'ช่วงหลัง', 'คงอยู่', 'ก่อน', 'ตลอดทั่วทั้ง', 'ทุกเมื่อ', 'อย่างไรก็ได้', 'ตั้ง', 'เพียง', 'เอา', 'มิฉะนั้น', 'สิ้น', 'พร้อมเพียง', 'จากนี้', 'นับจากนั้น', 'ทำให้', 'นํา', 'อะไร', 'อย่างเช่น', 'สิ่งนี้', 'ไง', 'สมัยก่อน', 'ตลอดศก', 'ส่วนเกิน', 'เท่านั้น', 'คงจะ', 'ส่วนใหญ่', 'แค่ว่า', 'บัดดล', 'ทั้ง', 'คราว', 'จริงๆจังๆ', 'เช่นนั้น', 'เท่า', 'เสร็จแล้ว', 'เมื่อวันวาน', 'ไว้', 'ถึงบัดนั้น', 'เกือบ', 'รวมกัน', 'ให้แก่', 'ด้วยกัน', 'เรา', 'พอจะ', 'ทุกครา', 'คุณๆ', 'พอแล้ว', 'หนอย', 'ยังแต่', 'พอสมควร', 'เช่นดังก่อน', 'สำคัญ', 'ยกให้', 'ยิ่งใหญ่', 'จนแม้น', 'ซะ', 'เพียงไหน', 'ทั้งสิ้น', 'ตลอดถึง', 'ก็ได้', 'กว้างขวาง', 'ช้า', 'เมื่อวาน', 'หารือ', 'ยืนนาน', 'รวมถึง', 'ใหญ่', 'ทันที', 'นั่นแหละ', 'เสียนั่น', 'ครั้งหลัง', 'เกิน', 'บางที่', 'จวบกับ', 'นานๆ', 'ครั้งๆ', 'ภายใต้', 'นำพา', 'เล็กๆ', 'ประกอบ', 'จนทั่ว', 'ช่วงถัดไป', 'แต่อย่างใด', 'ซึ่ง', 'พวกนู้น', 'หรือ', 'มันๆ', 'ตรง', 'บ้าง', 'กำหนด', 'ภายภาค', 'อยู่', 'จัด', 'ฉะนั้น', 'เช่นกัน', 'ถึงแม้ว่า', 'คราหนึ่ง', 'ด้วยเหตุนี้', 'เพียงพอ', 'ถ้า', 'นิดๆ', 'รับ', 'เฉยๆ', 'ยังโง้น', 'เป็นการ', 'แก', 'คง', 'เท่ากับ', 'บางครั้ง', 'อย่างดี', 'เมื่อเช้า', 'ยิ่งจน', 'เสร็จกัน', 'ผิดๆ', 'ล้วนจน', 'เช่นเดียวกัน', 'ตลอดกาลนาน', 'พวก', 'เถิด', 'เข้าใจ', 'ซะก่อน', 'เมื่อคราว', 'ใช้', 'อย่างไรเสีย', 'ภายภาคหน้า', 'ทีเดียว', 'ยังคง', 'คราวหนึ่ง', 'เป็นๆ', 'ถือว่า', 'คล้ายกันกับ', 'จึงเป็น', 'ค่อยๆ', 'อย่างนั้น', 'ทั้งนั้นด้วย', 'อันๆ', 'ส่วนใด', 'เช่นดังเก่า', 'ต่างก็', 'ฝ่ายใด', 'ทั้งหลาย', 'เป็นอันๆ', 'ทั้งที', 'มา', 'ในช่วง', 'แม้', 'ข้างต้น', 'ไม่ค่อย', 'และ', 'ส่วนมาก', 'พร้อมด้วย', 'อาจ', 'พวกมึง', 'สุด', 'มุ่งหมาย', 'เฉย', 'คราวหน้า', 'จวนเจียน', 'อย่างมาก', 'ยิ่งกว่า', 'แต่ไร', 'คราไหน', 'ฝ่าย', 'ประสบ', 'ยิ่งแล้ว', 'ทุกวันนี้', 'ใกล้ๆ', 'ดั่ง', 'ประการหนึ่ง', 'อาจจะ', 'สิ่งนั้น', 'ครั้งละ', 'ที่นั้น', 'เสียยิ่ง', 'เมื่อนั้น', 'เห็นว่า', 'เลย', 'แค่ไหน', 'เสียนั่นเอง', 'ภาคฯ', 'ครั้งกระนั้น', 'เพราะว่า', 'บัดนั้น', 'เราๆ', 'ส่วนน้อย', 'นั้น', 'ทําให้', 'ตลอดทั้ง', 'พวกมัน', 'คิด', 'ด้วยเพราะ', 'ตลอดวัน', 'เสีย', 'กันเอง', 'เผื่อว่า', 'เสียนี่กระไร', 'เต็มๆ', 'ที่แล้ว', 'แต่ต้อง', 'ยังไง', 'นอกจากนั้น', 'ทุกทาง', 'เหลือ', 'ทุกสิ่ง', 'เท่าที่', 'เสร็จสิ้น', 'เช่นนั้นเอง', 'อย่าง', 'นอกจากว่า', 'จัง', 'ให้ไป', 'เหตุ', 'ยืนยาว', 'ร่วมมือ', 'ก็จะ', 'ง่าย', 'สามารถ', 'เช่นใด', 'นี่', 'คิดว่า', 'จู่ๆ', 'พวกนี้', 'วันนี้', 'ครบครัน', 'ยิ่ง', 'ช่วงก่อน', 'วัน', 'พอเหมาะ', 'เพียงใด', 'เมื่อนี้', 'เป็นเพียง', 'ที่แท้จริง', 'ถูก', 'ๆ', 'ถูกต้อง', 'นอกจาก', 'ทั้งตัว', 'ส่ง', 'จัดทำ', 'พวกเขา', 'จรด', 'ใคร่จะ', 'นี่แหละ', 'เผื่อจะ', 'อย่างไร', 'เผื่อ', 'ค่อย', 'ความ', 'แยะ', 'กระผม', 'ยืนยง', 'จำพวก', 'อันจะ', 'เช่น', 'ผ่าน', 'ใหม่', 'กำลังจะ', 'นะ', 'เสียยิ่งนัก', 'หาก', 'ในเมื่อ', 'นอกจากที่', 'ทํา', 'ถึงอย่างไร', 'รึ', 'สมัย', 'ตลอดมา', 'เกือบๆ', 'อย่างหนึ่ง', 'ตลอดจน', 'จง', 'คราวนั้น', 'เพียงเพื่อ', 'ช่วง', 'อาจเป็น', 'ด้วยเหมือนกัน', 'เมื่อไร', 'ผู้ใด', 'ทั้งปวง', 'เมื่อคราวก่อน', 'ที่แท้', 'จะ', 'เริ่ม', 'หาความ', 'จัดหา', 'พอควร', 'เหตุนี้', 'ทั่ว', 'นาง', 'ด้าน', 'ตามด้วย', 'ตลอด', 'ที่นี้', 'เช่นดัง', 'ฉะนี้', 'ดังเก่า', 'ด้วยเหตุที่', 'ของ', 'ด้วยที่', 'นี้', 'ที่ไหน', 'ยิ่งจะ', 'น้อย', 'ดั่งกับว่า', 'จริง', 'ไม่ค่อยเป็น', 'กัน', 'เช่นก่อน', 'มากกว่า', 'คือ', 'พูด', 'พวกกู', 'บัดเดี๋ยวนี้', 'จำ', 'นักๆ', 'สูง', 'กับ', 'จวน', 'มากมาย', 'มิ', 'มึง', 'ครั้งคราว', 'เช่นที่ว่า', 'เนี่ย', 'ว่า', 'หนอ', 'ช่วย', 'เล็ก', 'จึง', 'ใกล้', 'สูงๆ', 'เช่นดังว่า', 'ระยะ', 'นิด', 'คราวๆ', 'ไม่ค่อยจะ', 'อย่างนี้', 'เกี่ยวกัน', 'กว้างๆ', 'ทุกวัน', 'แรก', 'แต่ละ', 'เพิ่ง', 'ช้านาน', 'ครบถ้วน', 'กันดีไหม', 'เสมือนว่า', 'เคยๆ', 'เล็กน้อย', 'ยาก', 'ให้', 'จนแม้', 'หากแม้นว่า', 'สุดๆ', 'ผ่านๆ', 'บอก', 'ล่าสุด', 'ค่อนมาทาง', 'แค่เพียง', 'ทั้งคน', 'แค่นี้', 'ค่ะ', 'ออก', 'วันนั้น', 'เห็น', 'ทรง', 'จนตลอด', 'ต่างหาก', 'จนขณะนี้', 'ทุกอย่าง', 'แต่เดิม', 'แยะๆ', 'ตามที่', 'ไร', 'นอกเหนือจาก', 'ที่ละ', 'ด้วย', 'รือ', 'อย่างยิ่ง', 'เร็วๆ', 'แก่', 'อย่างละ', 'เผื่อที่', 'พยายาม', 'เหตุผล', 'ดั่งกับ', 'ปัจจุบัน', 'อื่น', 'คำ', 'ครานั้น', 'หนึ่ง', 'เป็นอาทิ', 'อดีต', 'คราวไหน', 'เปิดเผย', 'แท้', 'แค่จะ', 'ที่ได้', 'ดังกับว่า', 'แค่', 'เป็นต้นไป', 'มุ่งเน้น', 'เมื่อคราวที่', 'แต่ที่', 'คราวที่', 'แต่ก่อน', 'แล้วแต่', 'พร้อมที่', 'น่ะ', 'หากว่า', 'ถึงแม้จะ', 'เอง', 'ต้อง', 'ที่ว่า', 'ช่วงท้าย', 'พื้นๆ', 'ภายหลัง', 'สมัยนั้น', 'เท่าไหร่', 'ทั้งนั้นเพราะ', 'เล่าว่า', 'สิ้นกาลนาน', 'นับ', 'ซะจน', 'ซึ่งกันและกัน', 'ส่วนดี', 'พอตัว', 'ครั้งหลังสุด', 'ภายหน้า', 'มัน', 'เป็นแต่เพียง', 'เห็นแก่', 'เช่นเคย', 'เหล่า', 'ช่วงนั้น', 'เป็นต้นมา', 'ยัง', 'แต่นั้น', 'เกี่ยวกับ', 'ครับ', 'เปลี่ยน', 'ตั้งแต่', 'นอกนั้น', 'เมื่อก่อน', 'นับจากนี้', 'เป็นเพราะว่า', 'ครานี้', 'ปรากฏว่า', 'มั้ยล่ะ', 'หาใช่', 'รวมทั้ง', 'ยิ่งเมื่อ', 'เช่นดังที่', 'ฯลฯ', 'จังๆ', 'พอดี', 'จนกระทั่ง', 'จวบจน', 'จ๊ะ', 'นัก', 'กลับ', 'แหละ', 'ได้รับ', 'เสร็จสมบูรณ์', 'จัดงาน', 'ยาวนาน', 'เปลี่ยนแปลง', 'ครั้งนั้น', 'รวม', 'อันเนื่องมาจาก', 'จวนจะ', 'ทั้งมวล', 'ขณะนั้น', 'ค่อนข้างจะ', 'เป็นอันมาก', 'ด้วยเหตุนั้น', 'ทุกครั้ง', 'คล้ายกับว่า', 'เฉกเช่น', 'ครั้ง', 'ราย', 'เช่นที่', 'แห่งนี้', 'เห็นจะ', 'แท้จริง', 'อยาก', 'เพื่อที่', 'ซึ่งก็คือ', 'เรื่อยๆ', 'ซึ่งๆ', 'ทุกแห่ง', 'ทุกๆ', 'บาง', 'ให้มา', 'ทั้งนี้', 'รับรอง', 'เกือบจะ', 'อย่างไหน', 'ทุกคราว', 'บางกว่า', 'คราวละ', 'ชาว', 'จ้า', 'เพียงเพราะ', 'ทุกคน', 'อัน', 'จาก', 'นอก', 'ผล', 'เพียงไร', 'ดั่งเก่า', 'เขียน', 'ก็ตาม', 'พวกแก', 'มั้ยนั่น', 'มั้ย', 'ตลอดไป', 'การ', 'นาน', 'หรือเปล่า', 'บ่อย', 'กระทำ', 'เรียก', 'ใหญ่โต', 'มุ่ง', 'เกิด', 'ขณะนี้', 'แห่งใด', 'ไม่เป็นไร', 'รวด', 'สั้น', 'สั้นๆ', 'จัดให้', 'กล่าวคือ', 'คุณ', 'ข้างบน', 'เดียวกัน', 'จนเมื่อ', 'นับตั้งแต่', 'ทุกที่', 'ปรากฏ', 'ถือ', 'อย่างไรก็', 'ในที่', 'ตนเอง', 'เป็นดัง', 'กลุ่มก้อน', 'ไหนๆ', 'ไป่', 'ส่วนด้อย', 'เท่าใด', 'เช่นนี้', 'ส่วนนั้น', 'เชื่อถือ', 'กันและกัน', 'รือว่า', 'ทว่า', 'ย่อม', 'เช่นไร', 'เหล่านั้น', 'ขวางๆ', 'ยิ่งขึ้นไป', 'คล้ายกัน', 'ไหน', 'นี่ไง', 'แก้ไข', 'ทุก', 'น้อยๆ', 'ล้วนแต่', 'ละ', 'พอกัน', 'ตลอดทั่ว', 'ต่อกัน', 'หากแม้', 'ช่วงๆ', 'พวกท่าน', 'ซึ่งได้แก่', 'ซะจนถึง', 'คราใด', 'เดียว', 'เยอะแยะ', 'ครั้งไหน', 'น่าจะ', 'บางขณะ', 'ไป', 'เฉพาะ', 'เคย', 'ขณะเดียวกัน', 'พวกเธอ', 'ซึ่งกัน', 'มีแต่', 'ร่วม', 'เสียแล้ว', 'ขณะใดๆ', 'นาย', 'คราวก่อน', 'แสดง', 'บัดนี้', 'อันใด', 'อันละ', 'ทุกชิ้น', 'เป็นอัน', 'นับแต่นี้', 'จับ', 'เสียจนกระทั่ง', 'ภายนอก', 'คราวใด', 'ค่อนข้าง', 'จน', 'คล้ายกับ', 'สําหรับ', 'ครั้งครา', 'ที่', 'พา', 'แห่ง', 'แสดงว่า', 'ผู้', 'พอ', 'ปรับ', 'ขั้น', 'เพิ่งจะ', 'เป็นเพื่อ', 'คราที่', 'เพื่อ', 'พวกคุณ', 'ตลอดกาล', 'จากนี้ไป', 'จัดตั้ง', 'ใหม่ๆ', 'ใน', 'มั๊ย', 'ยอม', 'ทั้งหมด', 'นี้แหล่', 'ถึงเมื่อไร', 'หน่อย', 'ที', 'จวบ', 'หมดกัน', 'นับแต่', 'ฉัน', 'จนกว่า', 'เป็นที', 'อย่างเดียว', 'ก็ตามที', 'บางครา', 'ทุกหน', 'เมื่อใด', 'ถึงเมื่อใด', 'เช่นที่เคย', 'ตน', 'ใคร', 'แต่ทว่า', 'ก็แล้วแต่', 'บอกว่า', 'แต่ก็', 'บ่อยครั้ง', 'อันได้แก่', 'จ้ะ', 'ยังจะ', 'ใครๆ', 'เท่าไร', 'พร้อมกับ', 'เท่ากัน', 'เรียบ', 'พวกโน้น', 'มั้ยเนี่ย', 'ไฉน', 'อย่างใด', 'วันไหน', 'พึ่ง', 'ตามแต่', 'ด้วยประการฉะนี้', 'แม้ว่า', 'ทั้งนั้น', 'ตาม', 'จนถึง', 'ถึงบัดนี้', 'ได้แต่'}) \n",
            "\n",
            "\n",
            "['ประโยค', 'ที่', 'ใช้', 'ใน', 'ภาษาไทย', ' ', 'และ', 'การ', 'เรียบเรียง', 'ประโยค', 'เป็น', 'เรื่องสั้น', ' ', 'ๆ', ' ', 'ช่วย', 'ให้', 'สามารถ', 'สื่อสาร', 'ได้', 'ถูกต้อง']\n",
            "['ประโยค', 'ภาษาไทย', ' ', 'เรียบเรียง', 'ประโยค', 'เรื่องสั้น', ' ', ' ', 'สื่อสาร']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Text Normalization"
      ],
      "metadata": {
        "id": "w60_aKEOAGl5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "tm_text = \"ติดต่อได้ที่ 092-346-5363 สมใหญ่\"\n",
        "print(\"newmm :\", word_tokenize(tm_text))\n",
        "\n",
        "tm_text_replace = tm_text.replace('-','')\n",
        "print(\"newmm :\", word_tokenize(tm_text_replace))\n",
        "\n",
        "for i in re.findall(r\"[\\d]{3}-[\\d]{3}-[\\d]{4}\", tm_text):\n",
        "  tm_text = tm_text.replace(i, 'PhoneNumber')\n",
        "print(\"newmm :\", word_tokenize(tm_text))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PyTB8uNg_woS",
        "outputId": "300af439-6458-4c87-ae6c-f328a218020f"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "newmm : ['ติดต่อ', 'ได้ที่', ' ', '092', '-', '346', '-', '5363', ' ', 'สม', 'ใหญ่']\n",
            "newmm : ['ติดต่อ', 'ได้ที่', ' ', '0923465363', ' ', 'สม', 'ใหญ่']\n",
            "newmm : ['ติดต่อ', 'ได้ที่', ' ', 'PhoneNumber', ' ', 'สม', 'ใหญ่']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "normalization"
      ],
      "metadata": {
        "id": "rUrfs08SCDfb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "from pythainlp import thai_tonemarks\n",
        "\n",
        "_NORMALIZE_RULE1 = [\n",
        "    \"ะ\",\n",
        "    \"ั\",\n",
        "    \"็\",\n",
        "    \"า\",\n",
        "    \"ิ\",\n",
        "    \"ี\",\n",
        "    \"ึ\",\n",
        "    \"่\",\n",
        "    \"ํ\",\n",
        "    \"ุ\",\n",
        "    \"ู\",\n",
        "    \"ใ\",\n",
        "    \"ไ\",\n",
        "    \"โ\",\n",
        "    \"ื\",\n",
        "    \"่\",\n",
        "    \"้\",\n",
        "    \"๋\",\n",
        "    \"๊\",\n",
        "    \"ึ\",\n",
        "    \"์\",\n",
        "    \"๋\",\n",
        "    \"ำ\",\n",
        "]  # เก็บพวกสระ วรรณยุกต์ที่ซ้ำกันแล้วมีปัญหา\n",
        "\n",
        "\n",
        "_NORMALIZE_RULE2 = [\n",
        "    (\"เเ\", \"แ\"),  # เ เ -> แ\n",
        "    (\"ํ(t)า\", \"\\\\1ำ\"),\n",
        "    (\"ํา(t)\", \"\\\\1ำ\"),\n",
        "    (\"([่-๋])([ัิ-ื])\", \"\\\\2\\\\1\"),\n",
        "    (\"([่-๋])([ูุ])\", \"\\\\2\\\\1\"),\n",
        "    (\"ำ([่-๋])\", \"\\\\1ำ\"),\n",
        "    (\"(์)([ัิ-ู])\", \"\\\\2\\\\1\"),\n",
        "]  # เก็บพวก พิมพ์ลำดับผิดหรือผิดแป้นแต่กลับแสดงผลถูกต้อง ให้ไปเป็นแป้นที่ถูกต้อง เช่น เ + เ ไปเป็น แ\n",
        "\n",
        "\n",
        "\n",
        "def normalize(text: str) -> str:\n",
        "    \"\"\"\n",
        "    Thai text normalize\n",
        "\n",
        "    :param str text: thai text\n",
        "    :return: thai text\n",
        "    **Example**::\n",
        "     >>> print(normalize(\"เเปลก\")==\"แปลก\") # เ เ ป ล ก กับ แปลก\n",
        "     True\n",
        "    \"\"\"\n",
        "    for data in _NORMALIZE_RULE2:\n",
        "        text = re.sub(data[0].replace(\"t\", \"[่้๊๋]\"), data[1], text)\n",
        "    for data in list(zip(_NORMALIZE_RULE1, _NORMALIZE_RULE1)):\n",
        "        text = re.sub(data[0].replace(\"t\", \"[่้๊๋]\") + \"+\", data[1], text)\n",
        "    return text"
      ],
      "metadata": {
        "id": "BgBKiGytChyw"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unnormalized_text = \"สวัสดีค้าบ วันนี้ได้เรียน NLP ก็สนุกดี น่าสนใจทั้งนั้น\"\n",
        "\n",
        "print(normalize(unnormalized_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBdFmLN_DS9K",
        "outputId": "845137bb-dc1b-4495-83c5-a3a063964647"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "สวัสดีค้าบ วันนี้ได้เรียน NLP ก็สนุกดี น่าสนใจทั้งนั้น\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spelling correction\n"
      ],
      "metadata": {
        "id": "EjtEyp5EDq_m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pythainlp\n",
        "\n",
        "misspell_text = 'ธนคาร'\n",
        "\n",
        "print(pythainlp.spell(misspell_text))\n",
        "print(pythainlp.correct(misspell_text))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMbFZZ0JDuxc",
        "outputId": "9fa51ec1-4dd6-4183-f946-4c9422cb2be5"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ธนาคาร', 'ธนสาร']\n",
            "ธนาคาร\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# vectorizer and machine learning in NLP"
      ],
      "metadata": {
        "id": "P-h67KXxGmi9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pythainlp[full]\n",
        "!pip install scikit-learn\n",
        "!pip install pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QG8HfHg4MJuY",
        "outputId": "445f56ff-a03b-432f-999c-d733eb88abba"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "train.txt: ไฟล์ข้อความ train\n",
        "\n",
        "train_label.txt: ไฟล์อธิบาย sentiment ของความ train\n",
        "\n",
        "test.txt: ไฟล์ข้อความ test\n",
        "\n",
        "test_label.txt: ไฟอธิบาย sentiment ของข้อความ train"
      ],
      "metadata": {
        "id": "BLhrp8HqMmKy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/train.txt\n",
        "!wget https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/train_label.txt\n",
        "!wget https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/test_label.txt\n",
        "!wget https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/test.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuGmEzMKMl-b",
        "outputId": "f464143a-4756-47d4-df43-51c6145a8fcb"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-06-29 03:56:27--  https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/train.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 5658270 (5.4M) [text/plain]\n",
            "Saving to: ‘train.txt.1’\n",
            "\n",
            "train.txt.1         100%[===================>]   5.40M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-06-29 03:56:28 (37.0 MB/s) - ‘train.txt.1’ saved [5658270/5658270]\n",
            "\n",
            "--2023-06-29 03:56:29--  https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/train_label.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 95216 (93K) [text/plain]\n",
            "Saving to: ‘train_label.txt.1’\n",
            "\n",
            "train_label.txt.1   100%[===================>]  92.98K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2023-06-29 03:56:30 (6.58 MB/s) - ‘train_label.txt.1’ saved [95216/95216]\n",
            "\n",
            "--2023-06-29 03:56:30--  https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/test_label.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10582 (10K) [text/plain]\n",
            "Saving to: ‘test_label.txt.1’\n",
            "\n",
            "test_label.txt.1    100%[===================>]  10.33K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-06-29 03:56:30 (67.0 MB/s) - ‘test_label.txt.1’ saved [10582/10582]\n",
            "\n",
            "--2023-06-29 03:56:30--  https://raw.githubusercontent.com/PyThaiNLP/wisesight-sentiment/master/kaggle-competition/test.txt\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 632776 (618K) [text/plain]\n",
            "Saving to: ‘test.txt.1’\n",
            "\n",
            "test.txt.1          100%[===================>] 617.95K  --.-KB/s    in 0.05s   \n",
            "\n",
            "2023-06-29 03:56:30 (12.0 MB/s) - ‘test.txt.1’ saved [632776/632776]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data preparation"
      ],
      "metadata": {
        "id": "TTnvBQ3HNeQr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df_train = pd.read_csv('/content/train.txt',header = None, on_bad_lines='skip')\n",
        "df_train_label = pd.read_csv('/content/train_label.txt',header = None, on_bad_lines='skip')\n",
        "\n",
        "\n",
        "df_test = pd.read_csv('/content/test.txt',header = None, on_bad_lines='skip')\n",
        "df_test_label = pd.read_csv('/content/test_label.txt',header = None, on_bad_lines='skip')"
      ],
      "metadata": {
        "id": "EwrmNE-iMbZh"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['label'] = df_train_label\n",
        "df_train.columns = ['text','label']\n",
        "df_train.dropna(inplace=True)\n",
        "\n",
        "df_test['label'] = df_test_label\n",
        "df_test.columns = ['text','label']\n"
      ],
      "metadata": {
        "id": "twrt5BeBQPCn"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "bQtCR3iW0uwS",
        "outputId": "b86ca0f2-20ee-4d58-d00d-9388841991d5"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text label\n",
              "0      ประเทศเราผลิตและส่งออกยาสูบเยอะสุดในโลกจิงป่าวคับ   neu\n",
              "1                                                     คะ   neu\n",
              "2                            อิเหี้ยออมทำกูอยากกินเอ็มเค   neg\n",
              "3                                                    😅😅😅   neu\n",
              "4                                สวัสดีวันพุธ แนน อะไรนะ   neu\n",
              "...                                                  ...   ...\n",
              "23424                                     แม่งควายล้วนนน   neu\n",
              "23425                   ดอยสุเทพน้องง ไปหมดแล้วววว #pm25   neg\n",
              "23426                           ค่าชุดอาจจะแพงกว่าส่วนลด   neu\n",
              "23427                              รัฐต้องการแค่ภาษีครับ   neg\n",
              "23428  ™ ได้พัฒนาระบบเข้าสู่รุ่นที่สองแล้ว มาดูกันสิว...   neg\n",
              "\n",
              "[23429 rows x 2 columns]"
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              "\n",
              "  <div id=\"df-06315020-192b-4a6f-890a-c276ca2f1879\">\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ประเทศเราผลิตและส่งออกยาสูบเยอะสุดในโลกจิงป่าวคับ</td>\n",
              "      <td>neu</td>\n",
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              "      <td>คะ</td>\n",
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              "      <td>neg</td>\n",
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              "      <th>3</th>\n",
              "      <td>😅😅😅</td>\n",
              "      <td>neu</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>สวัสดีวันพุธ แนน อะไรนะ</td>\n",
              "      <td>neu</td>\n",
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              "    <tr>\n",
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              "      <td>neu</td>\n",
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              "    <tr>\n",
              "      <th>23425</th>\n",
              "      <td>ดอยสุเทพน้องง ไปหมดแล้วววว #pm25</td>\n",
              "      <td>neg</td>\n",
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              "    <tr>\n",
              "      <th>23426</th>\n",
              "      <td>ค่าชุดอาจจะแพงกว่าส่วนลด</td>\n",
              "      <td>neu</td>\n",
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              "    <tr>\n",
              "      <th>23427</th>\n",
              "      <td>รัฐต้องการแค่ภาษีครับ</td>\n",
              "      <td>neg</td>\n",
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              "    <tr>\n",
              "      <th>23428</th>\n",
              "      <td>™ ได้พัฒนาระบบเข้าสู่รุ่นที่สองแล้ว มาดูกันสิว...</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "เซ็ตจำนวน data train test"
      ],
      "metadata": {
        "id": "2VzCs2Fu1AQs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train.shape)\n",
        "print(df_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01GaJuCY1Gag",
        "outputId": "26c412b4-8488-4105-d994-f0c625eb18d3"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(23429, 2)\n",
            "(2605, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_all_values = df_train.label.value_counts().to_frame()\n",
        "df_all_values.columns = ['label_train']\n",
        "df_all_values['label_test'] = df_test.label.value_counts()\n",
        "df_all_values['percentage_train'] = df_all_values.label_train.apply(lambda x : x /df_all_values.sum()[0])\n",
        "df_all_values['percentage_test'] = df_all_values.label_test.apply(lambda x : x /df_all_values.sum()[1])\n",
        "df_all_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "iX8jdFCb1vlu",
        "outputId": "49bac2fc-abfd-44db-b4f0-c69861280b8c"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     label_train  label_test  percentage_train  percentage_test\n",
              "neu        12753        1418          0.544325         0.544338\n",
              "neg         5982         665          0.255325         0.255278\n",
              "pos         4191         465          0.178881         0.178503\n",
              "q            503          57          0.021469         0.021881"
            ],
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              "      <th>q</th>\n",
              "      <td>503</td>\n",
              "      <td>57</td>\n",
              "      <td>0.021469</td>\n",
              "      <td>0.021881</td>\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
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    },
    {
      "cell_type": "code",
      "source": [
        "df_train.loc[df_train.label == 'q'].sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FOUMJGn026Ah",
        "outputId": "5e3a6ce1-eeb2-479c-d94b-22309596e71a"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text label\n",
              "15802                 แต่อินนิสฟรีไม่แถมของง่ะ ข้ามๆปัยย     q\n",
              "9054                     บัตร RED CARD ราคา 99 บาทค่ะ ^^     q\n",
              "16726  อะ แล้วแต่อะ กูโพสต์บอกอยากแดกเบียร์ทีไร เอาด้...     q\n",
              "339                              รอ MG6 ตัวใหม่ อย่างหรู     q\n",
              "7697   ลุ้นตั๋วหนังฟรีด่วนๆ กับ #Happymeal ชุด Buildi...     q"
            ],
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              "      <td>แต่อินนิสฟรีไม่แถมของง่ะ ข้ามๆปัยย</td>\n",
              "      <td>q</td>\n",
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              "      <th>9054</th>\n",
              "      <td>บัตร RED CARD ราคา 99 บาทค่ะ ^^</td>\n",
              "      <td>q</td>\n",
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              "    <tr>\n",
              "      <th>16726</th>\n",
              "      <td>อะ แล้วแต่อะ กูโพสต์บอกอยากแดกเบียร์ทีไร เอาด้...</td>\n",
              "      <td>q</td>\n",
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              "      <th>339</th>\n",
              "      <td>รอ MG6 ตัวใหม่ อย่างหรู</td>\n",
              "      <td>q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7697</th>\n",
              "      <td>ลุ้นตั๋วหนังฟรีด่วนๆ กับ #Happymeal ชุด Buildi...</td>\n",
              "      <td>q</td>\n",
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              "  </tbody>\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5f70d3cf-7cd9-430d-9c1e-e35e202f2278');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.loc[df_train.label == 'pos'].sample(5)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T8Ch0GmW3LiI",
        "outputId": "76c64934-36a9-493c-c8f2-5c74277c2ed8"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text label\n",
              "21145                       เคๆ กูพูดเล่น มึงก็จริงจัง55   pos\n",
              "22620                           ทำ ANESSA หาย เสียใจมาก😢   pos\n",
              "3783   สวยแบบ World Class! ลุคใหม่ของสาวสิงห์ ที่ดูดี...   pos\n",
              "10791                                กลัวบุหรี่ขายไม่ได้   pos\n",
              "13029                                            จริงอ่ะ   pos"
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              "      <th>3783</th>\n",
              "      <td>สวยแบบ World Class! ลุคใหม่ของสาวสิงห์ ที่ดูดี...</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d118ac80-9951-4e11-b7b0-04f558128c79')\"\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d118ac80-9951-4e11-b7b0-04f558128c79');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.loc[df_train.label == 'neg'].sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "caXuw_ox3QZD",
        "outputId": "f52605df-74a1-4109-8717-dc1061f29d85"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    text label\n",
              "14053  ทำไมเขาไม่ไปประท้วงสิงห์ ช้างเลิกผลิตเบียร์ตรง...   neg\n",
              "15860  โปรดช่วยเหลือชะนี ก่อนที่จะกลายเป็นนก?! ชะนีเป...   neg\n",
              "3811   เราเคยเจอเรียกแล้วไม่มา 3 คันติด เหมือนมันแรนด...   neg\n",
              "11261  ร่วมสนุกค่ะ.. ท้าคนมือไวหยุด มิตซูบิชิ ปาเจโรส...   neg\n",
              "14686  ชงเหล้าให้ไคฟ์และตัวเอง “ดื่มแบล็คไปก่อนนะไคฟ์...   neg"
            ],
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              "      <td>ทำไมเขาไม่ไปประท้วงสิงห์ ช้างเลิกผลิตเบียร์ตรง...</td>\n",
              "      <td>neg</td>\n",
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              "      <th>15860</th>\n",
              "      <td>โปรดช่วยเหลือชะนี ก่อนที่จะกลายเป็นนก?! ชะนีเป...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3811</th>\n",
              "      <td>เราเคยเจอเรียกแล้วไม่มา 3 คันติด เหมือนมันแรนด...</td>\n",
              "      <td>neg</td>\n",
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              "    <tr>\n",
              "      <th>11261</th>\n",
              "      <td>ร่วมสนุกค่ะ.. ท้าคนมือไวหยุด มิตซูบิชิ ปาเจโรส...</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14686</th>\n",
              "      <td>ชงเหล้าให้ไคฟ์และตัวเอง “ดื่มแบล็คไปก่อนนะไคฟ์...</td>\n",
              "      <td>neg</td>\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
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    {
      "cell_type": "code",
      "source": [
        "df_train.loc[df_train.label == 'neg'].sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MX1firVd3UI4",
        "outputId": "660580f8-1f52-4795-9dcc-179822a76b04"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 text label\n",
              "19277                               องุ่น ฅน ถ่าย รูป   neg\n",
              "21929  เชฟโรเลต ชูโกลบอลมาร์เก็ตติ้ง ขับเคลื่อนองค์กร   neg\n",
              "10510                                   ทำหน้าแบ๊วใส่   neg\n",
              "18533                          เอาแค่ MG3 ตัวท๊อบกะพอ   neg\n",
              "6538                                        ป่ะๆๆๆๆๆๆ   neg"
            ],
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              "\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
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              "      <th>19277</th>\n",
              "      <td>องุ่น ฅน ถ่าย รูป</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21929</th>\n",
              "      <td>เชฟโรเลต ชูโกลบอลมาร์เก็ตติ้ง ขับเคลื่อนองค์กร</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10510</th>\n",
              "      <td>ทำหน้าแบ๊วใส่</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18533</th>\n",
              "      <td>เอาแค่ MG3 ตัวท๊อบกะพอ</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6538</th>\n",
              "      <td>ป่ะๆๆๆๆๆๆ</td>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-db338854-39e2-4e31-af30-67a26dc35086')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-db338854-39e2-4e31-af30-67a26dc35086 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-db338854-39e2-4e31-af30-67a26dc35086');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# feature extraction bag-of-words(BOW) model"
      ],
      "metadata": {
        "id": "V21OENz73Yhl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Count Vectorizer"
      ],
      "metadata": {
        "id": "m-1kwqn03lRZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import pythainlp\n",
        "\n",
        "count_vec = CountVectorizer(tokenizer = pythainlp.tokenize.word_tokenize)\n",
        "cvec = count_vec.fit_transform(df_train.text)\n",
        "cvec_df = pd.DataFrame(cvec.toarray())\n",
        "print(cvec_df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QdlDHxsJ3pil",
        "outputId": "ffa4d748-7f6e-4649-ac98-fea344887abe"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(23429, 26528)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cvec_df.columns = count_vec.vocabulary_.keys()\n",
        "\n",
        "cvec_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "7kf4NYqr4cuj",
        "outputId": "4e8cd956-8af5-4bda-adad-92ee48ddd9d6"
      },
      "execution_count": 113,
      "outputs": [
        {
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          "data": {
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TF-IDF vectorizer"
      ],
      "metadata": {
        "id": "MH6cCijES6YK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer(tokenizer = pythainlp.tokenize.word_tokenize)\n",
        "tfidf_vec = tfidf.fit_transform(df_train.text).toarray()\n",
        "tfidf_df = pd.DataFrame(tfidf_vec)\n",
        "tfidf_vec.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_sDVl5OWS-zr",
        "outputId": "d417a0a7-d944-43ee-e077-7a83e83f19b6"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23429, 26528)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_df.columns = tfidf.vocabulary_.keys()\n",
        "tfidf_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "UG5114tKTI4j",
        "outputId": "be7b6f35-589a-4b5a-e19a-e35fa9e05ae7"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       ประเทศ  เรา  ผลิต  และ  ส่งออก     ยาสูบ  เยอะ  สุด   ใน  โลก  ...  \\\n",
              "0         0.0  0.0   0.0  0.0     0.0  0.000000   0.0  0.0  0.0  0.0  ...   \n",
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              "4         0.0  0.0   0.0  0.0     0.0  0.183858   0.0  0.0  0.0  0.0  ...   \n",
              "...       ...  ...   ...  ...     ...       ...   ...  ...  ...  ...  ...   \n",
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              "23427     0.0  0.0   0.0  0.0     0.0  0.000000   0.0  0.0  0.0  0.0  ...   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# supervised Machine Learning in NLP"
      ],
      "metadata": {
        "id": "7Y4Ua1NgF2G4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "text classification"
      ],
      "metadata": {
        "id": "M78T4ilgF8th"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df_train['text'], df_train['label'], random_state = 0)\n",
        "count_vect = CountVectorizer()\n",
        "X_train_counts = count_vect.fit_transform(X_train)\n",
        "tfidf_transformer = TfidfTransformer()\n",
        "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
        "clf = MultinomialNB().fit(X_train_tfidf, y_train)\n"
      ],
      "metadata": {
        "id": "sbMOsuHvF0BV"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression #\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import cross_val_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "models1 = [\n",
        "    RandomForestClassifier(n_estimators = 200, max_depth = 3, random_state = 0),\n",
        "    LinearSVC(random_state = 0),\n",
        "    MultinomialNB(),\n",
        "    LogisticRegression(random_state = 0)#\n",
        "]\n",
        "CV = 10\n",
        "cv_df = pd.DataFrame(index = range(CV * len(models1)))\n",
        "entries = []\n",
        "for model in models1:\n",
        "  model_name = model.__class__.__name__\n",
        "  accuracies = cross_val_score(model, count_vec.transform(df_test['text']), df_test['label'], scoring = 'accuracy', cv = CV)\n",
        "  for fold_idx, accuracy in enumerate(accuracies):\n",
        "    entries.append((model_name, fold_idx, accuracy))\n",
        "cv_df = pd.DataFrame(entries, columns=['model_name','fold_idx', 'accuracy'])\n",
        "\n",
        "sns.boxplot(x='model_name', y='accuracy', data= cv_df)\n",
        "sns.stripplot (x='model_name', y='accuracy', data= cv_df, size = 8, jitter = True, edgecolor = \"gray\", linewidth = 2)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gBKENiyFHo6z",
        "outputId": "fb16e457-f2ee-442d-c04f-dcd135de02fb"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ดู accuracy โดยรวมของทุกๆ model"
      ],
      "metadata": {
        "id": "8QNYyo46MTE2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cv_df.groupby('model_name').accuracy.mean().sort_values(ascending = False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TaiexeGMS32",
        "outputId": "fac14ff2-8be4-4354-bdec-530ffdea08b8"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "model_name\n",
              "RandomForestClassifier    0.544341\n",
              "MultinomialNB             0.541263\n",
              "LogisticRegression        0.454106\n",
              "LinearSVC                 0.398453\n",
              "Name: accuracy, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "model = LinearSVC(random_state = 0)\n",
        "\n",
        "model.fit(tfidf_vec, df_train.label)\n",
        "\n",
        "X_test = count_vec.transform(df_test['text'])\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "y_test = df_test.label\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n",
        "fig, ax = plt.subplots(figsize = (10,10))\n",
        "label_name = list(df_train.label.unique())\n",
        "\n",
        "sns.heatmap(conf_mat, annot = True, fmt = 'd', vmin = 0, vmax = 500, xticklabels= label_name, yticklabels= label_name)\n",
        "plt.ylabel('Actual')\n",
        "plt.xlabel('Predicted')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 867
        },
        "id": "GHympH3jMpUx",
        "outputId": "32d35f2b-bfa1-43e4-dc14-ec02874f8b71"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(metrics.classification_report(df_test.label, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ypKLD2ptTf6i",
        "outputId": "c8745351-bb1d-4868-a509-92651ce461cc"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         neg       0.27      0.29      0.28       665\n",
            "         neu       0.55      0.46      0.50      1418\n",
            "         pos       0.18      0.18      0.18       465\n",
            "           q       0.02      0.09      0.03        57\n",
            "\n",
            "    accuracy                           0.36      2605\n",
            "   macro avg       0.25      0.25      0.25      2605\n",
            "weighted avg       0.40      0.36      0.38      2605\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Unsupervised Machine learning in NLP\n"
      ],
      "metadata": {
        "id": "tQJ2g2TNTvG6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
        "from sklearn.decomposition import NMF, LatentDirichletAllocation\n",
        "\n",
        "no_features = 1000\n",
        "\n",
        "#NMF is able to use tf-idf\n",
        "tfidf_vectorizer = TfidfVectorizer(tokenizer = pythainlp.tokenize.word_tokenize, max_df=0.95, min_df=2, max_features=no_features, stop_words='english')\n",
        "tfidf = tfidf_vectorizer.fit_transform(df_train.text)\n",
        "tfidf_feature_names = tfidf_vectorizer.get_feature_names_out()\n",
        "\n",
        "\n",
        "\n",
        "# LDA can only use raw term counts for LDA because it is a probabilistic graphical model\n",
        "tf_vectorizer = CountVectorizer(tokenizer = pythainlp.tokenize.word_tokenize, max_df=0.95, min_df=2, max_features=no_features, stop_words='english')\n",
        "tf = tf_vectorizer.fit_transform(df_train.text)\n",
        "tf_feature_names = tf_vectorizer.get_feature_names_out()\n",
        "\n",
        "\n",
        "no_topics = 20\n",
        "nmf = NMF(n_components=no_topics, random_state=1, l1_ratio=.5, init='nndsvd').fit(tfidf)\n",
        "lda = LatentDirichletAllocation(n_components=no_topics, max_iter=5, learning_method='online', learning_offset=50.,random_state=0).fit(tf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJrGqpj-Tuq7",
        "outputId": "65f138c6-cca6-40f9-ef18-f3f30c526b4f"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def display_topics(model, feature_names, no_top_words):\n",
        "    for topic_idx, topic in enumerate(model.components_):\n",
        "        print(\"Topic {}:\".format(topic_idx))\n",
        "        print(\" \".join([feature_names[i] for i in topic.argsort()[:-no_top_words - 1:-1]]))\n",
        "\n",
        "\n",
        "no_top_words = 10\n"
      ],
      "metadata": {
        "id": "cW3SglRwUji_"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display_topics(nmf, tfidf_feature_names, no_top_words)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmKCo6J-aC58",
        "outputId": "a21a5065-7cda-41a2-fc11-3bc368671e86"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Topic 0:\n",
            "  - ใน 2 ( ) และ ที่ 3 กับ\n",
            "Topic 1:\n",
            "จะ ก็ ที่ มัน ว่า เป็น ให้ ได้ แต่ ของ\n",
            "Topic 2:\n",
            "กิน ลีโอ เค จะ เอ็ม พา 5555 ช้าง นน mk\n",
            "Topic 3:\n",
            "ไป พา กัน หน่อย กินกัน ไหน ด้วย ก่อน เอา หมด\n",
            "Topic 4:\n",
            "# ! สวย ท้า   ที่ กัน แป้ง ดี แดด\n",
            "Topic 5:\n",
            "ครับ ผม ขอ สู้ มาสด้า รถ นิสสัน หน่อย เท่าไหร่ ร่า\n",
            "Topic 6:\n",
            "ค่ะ ^^ สาขา ขอ นะคะ ร่วม มาก รายการ โลตัส ลูกค้า\n",
            "Topic 7:\n",
            "เบียร์ ช้าง สิงห์ ลีโอ ขวด แดก ยู พี่ ละ ดื่ม\n",
            "Topic 8:\n",
            "ๆ มาก สู้ กัน คับ พี่ ดี สุด ชอบ เฉย\n",
            "Topic 9:\n",
            "เลย มาก หิว นี้ เห็น ยัง ยย เหมือน นี่ จ้า\n",
            "Topic 10:\n",
            "มี สาขา มั้ย ที่ บ้าง โปร ไหน ตัง แต่ ด้วย\n",
            "Topic 11:\n",
            "อยาก กก จัง ลอง อ่ะ ให้ แดก กู เค้า ก\n",
            "Topic 12:\n",
            "นะ ด้วย คับ รอ นี้ ก่อน คร้าบ ขอ แต่ ดี\n",
            "Topic 13:\n",
            "ไม่ ได้ ยัง ใช่ แต่ ชอบ อ่ะ อร่อย เคย เอา\n",
            "Topic 14:\n",
            "555 เลี้ยง หิว แดก สิ ตัง มึง + ก่อน กว่า\n",
            "Topic 15:\n",
            ". / :// https www com th - http =\n",
            "Topic 16:\n",
            "ไหม คะ มั้ย ได้ คับ เท่าไหร่ ? ขอ ดี นี้\n",
            "Topic 17:\n",
            "จัด ต้อง 5555 นี้ วัน ให้ สิ ดิ วันนี้ มั้ย\n",
            "Topic 18:\n",
            "ก้อน บาบี พี่ น เลี้ยง บา บิ บี คร้าบ หมู\n",
            "Topic 19:\n",
            "มา แล้ว เอา กก ลอง รอ ซื้อ หมด วัน กัน\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Text visualization"
      ],
      "metadata": {
        "id": "8cG8U38vEfUv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://raw.githubusercontent.com/JagerV3/sentiment_analysis_thai/master/trainingdataset/burgerking-UTF8-traindataset-1.csv\n",
        "!wget https://raw.githubusercontent.com/JagerV3/sentiment_analysis_thai/master/trainingdataset/burgerking-UTF8-traindataset-2.csv\n",
        "!wget https://raw.githubusercontent.com/JagerV3/sentiment_analysis_thai/master/trainingdataset/burgerking-UTF8-traindataset-3.csv\n",
        "\n",
        "!pip install pythainlp[full]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzO0-ZlSEkIF",
        "outputId": "beedc477-7867-4138-f410-fb5325616a94"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-06-29 03:59:52--  https://raw.githubusercontent.com/JagerV3/sentiment_analysis_thai/master/trainingdataset/burgerking-UTF8-traindataset-1.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 19609 (19K) [text/plain]\n",
            "Saving to: ‘burgerking-UTF8-traindataset-1.csv.1’\n",
            "\n",
            "burgerking-UTF8-tra 100%[===================>]  19.15K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2023-06-29 03:59:52 (10.8 MB/s) - ‘burgerking-UTF8-traindataset-1.csv.1’ saved [19609/19609]\n",
            "\n",
            "--2023-06-29 03:59:52--  https://raw.githubusercontent.com/JagerV3/sentiment_analysis_thai/master/trainingdataset/burgerking-UTF8-traindataset-2.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 24962 (24K) [text/plain]\n",
            "Saving to: ‘burgerking-UTF8-traindataset-2.csv.1’\n",
            "\n",
            "burgerking-UTF8-tra 100%[===================>]  24.38K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2023-06-29 03:59:52 (15.4 MB/s) - ‘burgerking-UTF8-traindataset-2.csv.1’ saved [24962/24962]\n",
            "\n",
            "--2023-06-29 03:59:52--  https://raw.githubusercontent.com/JagerV3/sentiment_analysis_thai/master/trainingdataset/burgerking-UTF8-traindataset-3.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 31895 (31K) [text/plain]\n",
            "Saving to: ‘burgerking-UTF8-traindataset-3.csv.1’\n",
            "\n",
            "burgerking-UTF8-tra 100%[===================>]  31.15K  --.-KB/s    in 0.002s  \n",
            "\n",
            "2023-06-29 03:59:52 (12.7 MB/s) - ‘burgerking-UTF8-traindataset-3.csv.1’ saved [31895/31895]\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard>=1.14->thai-nner->pythainlp[full]) (3.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import re\n",
        "import string\n",
        "from pythainlp.corpus import thai_stopwords"
      ],
      "metadata": {
        "id": "L6h2hrbhEqSv"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('burgerking-UTF8-traindataset-1.csv')\n",
        "\n",
        "import re\n",
        "import string\n",
        "from pythainlp.corpus.common import thai_stopwords\n",
        "\n",
        "def clean_text(txt):\n",
        "\n",
        "\n",
        "    # ลบ text ที่อยู่ในวงเล็บ <> ทั้งหมด\n",
        "    txt = re.sub(r'<.@*?!>','', txt)\n",
        "\n",
        "    # ลบ hashtag\n",
        "    txt = re.sub(r'#','',txt)\n",
        "\n",
        "    # ลบ space\n",
        "    txt = re.sub(r' ','',txt)\n",
        "\n",
        "    #ลบ เลข\n",
        "    txt = re.sub(\"(\\d)\", \"\",txt)\n",
        "\n",
        "    # ลบ เครื่องหมายคำพูด (punctuation)\n",
        "    for c in string.punctuation:\n",
        "        txt = re.sub(r'\\{}'.format(c),'',txt)\n",
        "\n",
        "    # ลบ separator เช่น \\n \\t\n",
        "    txt = ' '.join(txt.split())\n",
        "\n",
        "\n",
        "    return txt\n",
        "\n",
        "df.message = df.message.apply(clean_text)"
      ],
      "metadata": {
        "id": "dt9ZXqGnFGy5"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = ['label','message']\n",
        "df[\"label\"].replace({0: \"negative\", 1: \"postive\"}, inplace=True)"
      ],
      "metadata": {
        "id": "Xy_km2BYFSIt"
      },
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# understand Data with exploratory data analysis"
      ],
      "metadata": {
        "id": "k9H5uYKeFXVJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.label.value_counts().plot.barh()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "A7hIj-jJFfkS",
        "outputId": "504d6872-c3a1-426f-980b-ec644a191728"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.displot(df.message.apply(len))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "J6Mg-1ZnGJn2",
        "outputId": "dd6c9641-1e72-4765-9e04-d4fef2249ff2"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fae458c6aa0>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "# sort the data frame by target\n",
        "target_0 = df.loc[df['label'] == 'negative']\n",
        "target_1 = df.loc[df['label'] == 'positive']\n",
        "\n",
        "sns.distplot(target_0.message.apply(len), hist = True, label = 'negative')\n",
        "sns.distplot(target_1.message.apply(len), hist = True, label = 'positive')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "id": "3RLbb8cTGZl9",
        "outputId": "3333a039-86fe-4fbe-cdb5-5d173d048d93"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-133-4b2c1bab19ba>:9: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(target_0.message.apply(len), hist = True, label = 'negative')\n",
            "<ipython-input-133-4b2c1bab19ba>:10: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(target_1.message.apply(len), hist = True, label = 'positive')\n",
            "/usr/local/lib/python3.10/dist-packages/seaborn/distributions.py:2479: RuntimeWarning: Mean of empty slice.\n",
            "  line, = ax.plot(a.mean(), 0)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  ret = ret.dtype.type(ret / rcount)\n",
            "/usr/local/lib/python3.10/dist-packages/numpy/lib/histograms.py:906: RuntimeWarning: invalid value encountered in true_divide\n",
            "  return n/db/n.sum(), bin_edges\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pythainlp.util import find_keyword\n",
        "from pythainlp.util import rank\n",
        "from pythainlp.tokenize import word_tokenize\n",
        "\n",
        "def get_text_list(message):\n",
        "  tokenized = []\n",
        "  for i in message:\n",
        "    token = word_tokenize(i)\n",
        "    for j in token:\n",
        "      tokenized.append(j)\n",
        "  return tokenized\n",
        "\n",
        "text = get_text_list(df.message)\n",
        "#rank(text)\n",
        "\n",
        "find_keyword(text, min_len = 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uz9P7hPsHU4j",
        "outputId": "a6fc5c05-e384-4790-e438-de7fd6b0c248"
      },
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'สาขา': 22,\n",
              " 'ซื้อ': 3,\n",
              " 'พาย': 4,\n",
              " 'ไก่': 4,\n",
              " 'ชิ้น': 6,\n",
              " 'ทาน': 4,\n",
              " 'บริการ': 14,\n",
              " 'ดี': 11,\n",
              " 'โพ': 3,\n",
              " 'พารากอน': 3,\n",
              " 'คน': 7,\n",
              " 'คุณภาพ': 5,\n",
              " 'อาหาร': 10,\n",
              " 'กิน': 16,\n",
              " 'สอง': 3,\n",
              " 'แย่': 3,\n",
              " 'แบบนี้': 4,\n",
              " 'ร้าน': 4,\n",
              " 'BurgerKing': 7,\n",
              " 'พนักงาน': 21,\n",
              " 'ออเดอร์': 3,\n",
              " 'สั่ง': 9,\n",
              " 'เฟรนฟ': 6,\n",
              " 'บอ': 5,\n",
              " 'เดี๋ยว': 3,\n",
              " 'โต๊ะ': 6,\n",
              " 'นั่ง': 3,\n",
              " 'รอ': 3,\n",
              " 'ผู้จัดการ': 4,\n",
              " 'เหมือน': 5,\n",
              " ' ': 25,\n",
              " 'เส': 5,\n",
              " 'ริ': 5,\n",
              " 'ฟ': 4,\n",
              " 'รอบ': 3,\n",
              " 'ห่อ': 3,\n",
              " 'พี่': 3,\n",
              " 'ลูกค้า': 12,\n",
              " 'เบอร์เกอร์': 7,\n",
              " 'ชอบ': 10,\n",
              " 'เจอ': 3,\n",
              " 'คิง': 3,\n",
              " 'แก้ว': 4,\n",
              " 'ทอง': 3,\n",
              " 'หล่อ': 3,\n",
              " 'ทราบ': 3,\n",
              " 'ทำ': 6,\n",
              " 'อร่อย': 10,\n",
              " 'ขาย': 3,\n",
              " 'ปรับปรุง': 7,\n",
              " 'ผม': 3,\n",
              " 'ขอให้': 4,\n",
              " 'เนื้อ': 3,\n",
              " 'แผ่น': 4,\n",
              " 'เลี่ยน': 3,\n",
              " 'ขอบคุณ': 3,\n",
              " 'หัวหน้างาน': 4,\n",
              " 'น้ำเสียง': 3,\n",
              " 'ใส่': 3,\n",
              " 'ประทับใจ': 3,\n",
              " 'น่ารัก': 4,\n",
              " 'กก': 8}"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(rank(text).values())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "elOXKBZZIU8W",
        "outputId": "2916ae61-6f05-476a-cf63-c0ff9dba69b8"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fae462e36a0>"
            ]
          },
          "metadata": {},
          "execution_count": 135
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 621x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# text clound"
      ],
      "metadata": {
        "id": "gt_buR1tIhAv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from wordcloud import WordCloud"
      ],
      "metadata": {
        "id": "-ZwwImWIIgXa"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regexp = r\"[ก-๙a-zA-Z']+\""
      ],
      "metadata": {
        "id": "L757syWBJHN6"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pythainlp.corpus import thai_stopwords\n",
        "from pythainlp.tokenize import word_tokenize\n",
        "\n",
        "def get_text_str(message):\n",
        "  tokenized = \" \"\n",
        "  th_stw = thai_stopwords()\n",
        "  for i in message:\n",
        "    token = word_tokenize(i)\n",
        "    for j in token:\n",
        "      if j not in thai_stopwords():\n",
        "        tokenized = tokenized + \" \" + j\n",
        "  return tokenized\n",
        "\n",
        "text = get_text_str(df.message)\n"
      ],
      "metadata": {
        "id": "VONNo4RSJLYG"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.message"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFXqVkjaKSIZ",
        "outputId": "345a92ca-605e-46be-945d-f39e4eceaa27"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     นไปที่สาขาในปั๊มคาลเท็กซ์ประชานุกูลซื้อพายไก่แ...\n",
              "1     BKทุกสาขาบริการดีโดยเฉพาะอย่างยิ่งเอมโพเรียมแม...\n",
              "2     BurgerKingสาขาTheBrightพระรามบริการห่วยมากถึงม...\n",
              "3     BurgerKingสาขาปั้มปตทบ้านพรอถลาง เมื่อคืนวันจั...\n",
              "4        chickenstripชิ้นเล็กมากเทียบกับป้ายโฆษณาบนโต๊ะ\n",
              "5     Deliveryไม่ทั่วถึงสาขาที่ใกล้เราที่สุดอยู่ทองห...\n",
              "6     กิจกรรมที่จัดห่วยแตกได้รางวัลไม่แจ้งทางอินบ๊อก...\n",
              "7     ขอแก้ไขรีวิวล่าสุดที่ไปมาตอนนี้อร่อยทุกอย่างแล...\n",
              "8     ขอคอมเม้นต์นิดนึงครับวันนี้ผมมากินที่สาขาทางด่...\n",
              "9     ขอบคุณที่ร่วมแชร์ประสบการณ์แย่ๆนะครับ สาขาEsso...\n",
              "10    ขอร้องเรียนSupervisorผู้หญิงที่สาขาcentralworl...\n",
              "11    ควรที่จะต้องปรับปรุงมารยาทพนักงานโดยเฉพาะที่สา...\n",
              "12    ควรใส่ใจในการตรวจสอบสินค้ามากกว่านี้ไปมารอบขาด...\n",
              "13    คืนวันที่มคไปใช้บริการBurgerKingสาขารามอินทราป...\n",
              "14    ช่วงทุ่มไปใช้บริการที่หน้าร้านลูกค้าต่อคิวกันย...\n",
              "15    ดูภาพwhopperที่ได้เทียบกับโฆษณาแล้วเอาไปปรับปร...\n",
              "16    เดี๋ยวนี้คุณภาพรสชาติตกลงเยอะมากเบอร์เกอร์ที่ไ...\n",
              "17    เดี๋ยวนี้เฟรนฟรายเค็มมากค่ะ ไม่รู้ว่าเกลือถูกห...\n",
              "18                     ชอบมากอยากให้มีที่ปราจีนบุรีด้วย\n",
              "19                burgerอร่อยบรรยากาศร้านดีมีเสียงดนตรี\n",
              "20                    crunchybbqbaconwhopperอร่อยมากๆๆๆ\n",
              "21                            Hlohaชอบมากๆอร่อยทุกอย่าง\n",
              "22    กินบ่อยค่ะBurgerKingหลายสาขาค่ะแถวสยามทองหล่อแ...\n",
              "23                         กินประจำเลยค่ะเนื้ออร่อยฟุดๆ\n",
              "24            ของชอบมากครับอร่อยมากครับราคาก็ไม่แพงครับ\n",
              "25                               ของน่ากินคะ่ไม้แพงด้วย\n",
              "26    ขอบคุณมากเลยพนักงานใจดีสาขา เดอมอล์บางกะปิพี่ท...\n",
              "27    คุ้มค่าคุ้มราคารับฟังพรัอมเเก้ปันหาดีต่อใจที่ส...\n",
              "28            เคยกินทีหัวหินหร่อยไปเลยแต่สาขาน้อยไปฮ่าๆ\n",
              "29    งํ่าๆอร่อยครับชิ้นเดียวอิ่มชัวร์มาสคอตน่ารักด้...\n",
              "30                                             เจ๋งครับ\n",
              "31                                               ฉันชอบ\n",
              "32                               ชอบอร่อยชิ้นหญ่ม๊ากกกก\n",
              "33    ชอบburgerkingมากๆอยู่แล้วแต่ล่าสุดชามะนาวหวานม...\n",
              "34      ชอบค่ะกลางคืนโอเคเลยนะคนไม่เยอะ พนักงานบริการดี\n",
              "35    ชอบใช้ที่MBKชั้นค่ะประทับใจหลากหลายทั้งสะดวกบร...\n",
              "Name: message, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://www.f0nt.com/download/sipafonts/THSarabunNew.zip ; unzip THSarabunNew.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DMSSDUMfKbTB",
        "outputId": "7709fd19-00f8-4256-ba6c-52f81847d42e"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-06-29 04:03:11--  https://www.f0nt.com/download/sipafonts/THSarabunNew.zip\n",
            "Resolving www.f0nt.com (www.f0nt.com)... 103.246.18.21, 2401:5700:100:503::1821\n",
            "Connecting to www.f0nt.com (www.f0nt.com)|103.246.18.21|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://www.f0nt.com/files/sipafonts/THSarabunNew.zip [following]\n",
            "--2023-06-29 04:03:13--  https://www.f0nt.com/files/sipafonts/THSarabunNew.zip\n",
            "Reusing existing connection to www.f0nt.com:443.\n",
            "HTTP request sent, awaiting response... "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = 'THSarabunNew.ttf'\n",
        "wordcloud = WordCloud(\n",
        "                      font_path=path,\n",
        "                      min_font_size=1,\n",
        "                      background_color=\"white\",\n",
        "                      width=400,\n",
        "                      height=200,\n",
        "                      max_words=1000,\n",
        "                      colormap='plasma',\n",
        "                      scale=3,\n",
        "                      font_step=4,\n",
        "                    #   contour_width=3,\n",
        "                      contour_color='steelblue',\n",
        "                      collocations=False,\n",
        "                      regexp=regexp,\n",
        "                      margin=2\n",
        "                      ).generate(text)"
      ],
      "metadata": {
        "id": "AEhs3C8xKoIg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize = (16, 12))\n",
        "ax.imshow(wordcloud, interpolation = 'bilinear')\n",
        "ax.axis(\"off\")\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "M-dVUAVvKtxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Scatter Text"
      ],
      "metadata": {
        "id": "IsM5QYafLEIe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['tokenized'] = df.message.apply(word_tokenize)"
      ],
      "metadata": {
        "id": "an7XWplQLS2P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "\n",
        "neg = df.loc[df.label == \"negative\"].tokenized\n",
        "pos = df.loc[df.label == \"positive\"].tokenized\n",
        "all = df.tokenized\n",
        "\n",
        "count_neg = []\n",
        "count_pos = []\n",
        "count_all = []\n",
        "\n",
        "for i in neg:\n",
        "  for j in i:\n",
        "    count_neg.append(j)\n",
        "for i in pos:\n",
        "  for j in i:\n",
        "    count_pos.append(j)\n",
        "for i in all:\n",
        "  for j in i:\n",
        "    count_all.append(j)\n",
        "\n",
        "counter_pos=collections.Counter(count_neg)\n",
        "counter_neg=collections.Counter(count_pos)\n",
        "counter_all=collections.Counter(count_all)\n",
        "\n",
        "your_list_neg = [list(i) for i in counter_neg.items()]\n",
        "your_list_pos = [list(i) for i in counter_pos.items()]\n",
        "your_list = [list(i) for i in counter_all.items()]\n",
        "\n",
        "count = pd.DataFrame(your_list,columns=['Word','Counts'])\n",
        "count_neg = pd.DataFrame(your_list_neg,columns=['Word','Counts_positive'])\n",
        "count_pos = pd.DataFrame(your_list_pos,columns=['Word','Counts_negative'])\n",
        "\n",
        "\n",
        "result = pd.merge(pd.merge(count,count_neg,on='Word'),count_pos,on='Word')"
      ],
      "metadata": {
        "id": "oLVAsBG1LdDu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!wget -q http://www.arts.chula.ac.th/ling/wp-content/uploads/TH-Sarabun_Chula1.1.zip -O font.zip\n",
        "!unzip -qj font.zip TH-Sarabun_Chula1.1/THSarabunChula-Regular.ttf\n",
        "# !pip install -U --pre matplotlib\n",
        "import matplotlib as mpl\n",
        "mpl.font_manager.fontManager.addfont('THSarabunChula-Regular.ttf')\n",
        "mpl.rc('font', family='TH Sarabun Chula')\n",
        "\n",
        "import matplotlib.font_manager as fm\n",
        "from matplotlib import rcParams\n",
        "x = result.Counts_negative.tolist()\n",
        "y=result.Counts_positive.tolist()\n",
        "\n",
        "\n",
        "plt.rcParams['xtick.labelsize'] = 20.0\n",
        "plt.rcParams['ytick.labelsize'] = 20.0\n",
        "plt.figure(figsize=(20, 20))\n",
        "rcParams.update({'font.size': 22})\n",
        "plt.scatter(x, y,s=1)\n",
        "plt.xlim(0,20)\n",
        "plt.ylim(0,20)\n",
        "\n",
        "\n",
        "for i, txt in enumerate(result.Word.tolist()):\n",
        "    plt.annotate(txt, (x[i], y[i]))\n",
        "plt.savefig('foo.pdf')"
      ],
      "metadata": {
        "id": "7KDoxdhrM2zW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Co-Occurance Analysis"
      ],
      "metadata": {
        "id": "tV3eEtSWM4be"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install pythainlp[full]\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pythainlp.tokenize import word_tokenize\n",
        "\n",
        "def create_cooccurrence_matrix(sentences, window_size=4):\n",
        "    \"\"\"Create co occurrence matrix from given list of sentences.\n",
        "\n",
        "    Returns:\n",
        "    - vocabs: dictionary of word counts\n",
        "    - co_occ_matrix_sparse: sparse co occurrence matrix\n",
        "\n",
        "    Example:\n",
        "    ===========\n",
        "    sentences = ['I love nlp',    'I love to learn',\n",
        "                 'nlp is future', 'nlp is cool']\n",
        "\n",
        "    vocabs,co_occ = create_cooccurrence_matrix(sentences)\n",
        "\n",
        "    df_co_occ  = pd.DataFrame(co_occ.todense(),\n",
        "                              index=vocabs.keys(),\n",
        "                              columns = vocabs.keys())\n",
        "\n",
        "    df_co_occ = df_co_occ.sort_index()[sorted(vocabs.keys())]\n",
        "\n",
        "    df_co_occ.style.applymap(lambda x: 'color: red' if x>0 else '')\n",
        "\n",
        "    \"\"\"\n",
        "    import scipy\n",
        "    import nltk\n",
        "\n",
        "    vocabulary = {}\n",
        "    data = []\n",
        "    row = []\n",
        "    col = []\n",
        "\n",
        "    tokenizer = word_tokenize\n",
        "    #tokenizer = nltk.tokenize.word_tokenize\n",
        "\n",
        "    for sentence in sentences:\n",
        "        sentence = sentence.strip()\n",
        "\n",
        "        tokens = [token for token in tokenizer(sentence) if token != u\"\"]\n",
        "        for pos, token in enumerate(tokens):\n",
        "            i = vocabulary.setdefault(token, len(vocabulary))\n",
        "            start = max(0, pos-window_size)\n",
        "            end = min(len(tokens), pos+window_size+1)\n",
        "            for pos2 in range(start, end):\n",
        "                if pos2 == pos:\n",
        "                    continue\n",
        "                j = vocabulary.setdefault(tokens[pos2], len(vocabulary))\n",
        "                data.append(1.)\n",
        "                row.append(i)\n",
        "                col.append(j)\n",
        "\n",
        "    cooccurrence_matrix_sparse = scipy.sparse.coo_matrix((data, (row, col)))\n",
        "    return vocabulary, cooccurrence_matrix_sparse\n"
      ],
      "metadata": {
        "id": "5gPBB02pNFbg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#import nltk\n",
        "#nltk.download('punkt')\n",
        "#sentences = ['I love nlp',    'I love to learn',\n",
        "#             'nlp is future', 'nlp is cool']\n",
        "\n",
        "sentences = df.message\n",
        "vocabs,co_occ = create_cooccurrence_matrix(sentences)\n",
        "\n",
        "df_co_occ  = pd.DataFrame(co_occ.todense(),\n",
        "                          index=vocabs.keys(),\n",
        "                          columns = vocabs.keys())\n",
        "\n",
        "df_co_occ = df_co_occ.sort_index()[sorted(vocabs.keys())]\n",
        "\n",
        "df_co_occ.style.applymap(lambda x: 'color: red' if x>0 else '')\n"
      ],
      "metadata": {
        "id": "XbkhUWnON4Uw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_co_occ"
      ],
      "metadata": {
        "id": "PR_efmzrN6wn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install networkx\n",
        "# importing networkx\n",
        "import networkx as nx\n",
        "# importing matplotlib.pyplot\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager as fm\n",
        "font_list = fm.createFontList(['THSarabunNew.ttf'])\n",
        "fm.fontManager.ttflist.extend(font_list)\n",
        "\n"
      ],
      "metadata": {
        "id": "GIshRaiFOH4F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visual_co_occ_matrix(keyword,top_co_occ = 10):\n",
        "  ### keyword เป็น str ###\n",
        "  graph = nx.Graph()\n",
        "  labels_params = {\"font_family\": \"TH Sarabun Chula\",\n",
        "                 \"alpha\":1,\n",
        "                 \"font_size\":20,\n",
        "                 \"with_labels\": True}\n",
        "  word =[]\n",
        "\n",
        "  for i in df_co_occ.loc[keyword].sort_values(ascending = False).reset_index()['index'][:top_co_occ]:\n",
        "    if i not in thai_stopwords():\n",
        "      graph.add_edge(keyword,i)\n",
        "      word.append(i)\n",
        "      for j in df_co_occ.loc[i].sort_values(ascending = False).reset_index()['index'][:top_co_occ]:\n",
        "        if j not in thai_stopwords():\n",
        "          graph.add_edge(i,j)\n",
        "\n",
        "  return nx.draw(graph,node_color='#00b4d9', **labels_params)"
      ],
      "metadata": {
        "id": "7ibKLBI3QmQy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visual_co_occ_matrix('BurgerKing')"
      ],
      "metadata": {
        "id": "72XijonfQoJf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}