Sentiment Analysis for Mental Health

Overview

The project uses a dataset of mental health statements tagged with various mental health statuses on kaggle. It includes data preprocessing, exploratory data analysis, model training, and evaluation.

Steps

• Data cleaning and preprocessing
• Exploratory Data Analysis (EDA)
• Text preprocessing using spaCy
• TF-IDF vectorization
• Oversampling using SMOTE
• Multiple classification models comparison
• Model evaluation and fine-tuning
• Model Saving

Requirements

• Python 3.11.x
• Libraries: numpy, pandas, scikit-learn, spacy, imbalanced-learn, matplotlib, seaborn, tqdm, pickle, scipy

Installation

1. Clone this repository
2. Install required packages:

   pip install -r requirements.txt
   

3. Download the spaCy English model:

   python -m spacy download en_core_web_sm
   

Usage

1. Run the Jupyter notebook Sentiment_Analysis.ipynb
2. The notebook will guide you through the entire process from data loading to model evaluation

Models Evaluated

• Logistic Regression
• Decision Tree Classifier
• Extra Tree Classifier
• AdaBoost Classifier
• Random Forest Classifier
• Extra Trees Classifier
• Gradient Boosting Classifier
• Bagging Classifier
• SGD Classifier
• SVC
• MLP Classifier

Fine-tuning

The project includes fine-tuning of the best-performing model using RandomizedSearchCV.

Contributors

• Piyawat Nulek ([email protected])