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<div class="quarto-title-block"><div><h1 class="title">Systematic literature review</h1><button type="button" class="btn code-tools-button dropdown-toggle" id="quarto-code-tools-menu" data-bs-toggle="dropdown" aria-expanded="false"><i class="bi"></i> Code</button><ul class="dropdown-menu dropdown-menu-end" aria-labelelledby="quarto-code-tools-menu"><li><a id="quarto-show-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Show All Code</a></li><li><a id="quarto-hide-all-code" class="dropdown-item" href="javascript:void(0)" role="button">Hide All Code</a></li><li><hr class="dropdown-divider"></li><li><a id="quarto-view-source" class="dropdown-item" href="javascript:void(0)" role="button">View Source</a></li></ul></div></div>
<p class="subtitle lead">Topic modeling</p>
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<div class="quarto-title-meta-author">
<div class="quarto-title-meta-heading">Authors</div>
<div class="quarto-title-meta-heading">Affiliation</div>
<div class="quarto-title-meta-contents">
<p class="author">Olivier Caron </p>
</div>
<div class="quarto-title-meta-contents">
<p class="affiliation">
Paris Dauphine - PSL
</p>
</div>
<div class="quarto-title-meta-contents">
<p class="author">Christophe Benavent </p>
</div>
<div class="quarto-title-meta-contents">
<p class="affiliation">
Paris Dauphine - PSL
</p>
</div>
</div>
<div class="quarto-title-meta">
<div>
<div class="quarto-title-meta-heading">Published</div>
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<p class="date">October 4, 2023</p>
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<nav id="TOC" role="doc-toc" class="toc-active">
<h2 id="toc-title">Table of contents</h2>
<ul>
<li><a href="#libraries-r" id="toc-libraries-r" class="nav-link active" data-scroll-target="#libraries-r"><span class="header-section-number">1</span> Libraries R</a></li>
<li><a href="#loading-data-r" id="toc-loading-data-r" class="nav-link" data-scroll-target="#loading-data-r"><span class="header-section-number">2</span> Loading data R</a></li>
<li><a href="#a-glimpse-of-data" id="toc-a-glimpse-of-data" class="nav-link" data-scroll-target="#a-glimpse-of-data"><span class="header-section-number">3</span> A glimpse of data</a></li>
<li><a href="#python-libraries-and-loading-data" id="toc-python-libraries-and-loading-data" class="nav-link" data-scroll-target="#python-libraries-and-loading-data"><span class="header-section-number">4</span> Python libraries and loading data</a></li>
<li><a href="#cuda-status-and-device-info" id="toc-cuda-status-and-device-info" class="nav-link" data-scroll-target="#cuda-status-and-device-info"><span class="header-section-number">5</span> CUDA Status and Device Info</a></li>
<li><a href="#detect-interpretable-topics-with-bertopic" id="toc-detect-interpretable-topics-with-bertopic" class="nav-link" data-scroll-target="#detect-interpretable-topics-with-bertopic"><span class="header-section-number">6</span> Detect interpretable topics with BERTopic</a>
<ul class="collapse">
<li><a href="#function-to-create-bertopics-with-custom-embeddings" id="toc-function-to-create-bertopics-with-custom-embeddings" class="nav-link" data-scroll-target="#function-to-create-bertopics-with-custom-embeddings"><span class="header-section-number">6.1</span> Function to create BERTopics with custom embeddings</a></li>
<li><a href="#function-to-visualize-bertopics" id="toc-function-to-visualize-bertopics" class="nav-link" data-scroll-target="#function-to-visualize-bertopics"><span class="header-section-number">6.2</span> Function to visualize BERTopics</a></li>
<li><a href="#create-topics-for-a-list-of-embeddings-model" id="toc-create-topics-for-a-list-of-embeddings-model" class="nav-link" data-scroll-target="#create-topics-for-a-list-of-embeddings-model"><span class="header-section-number">6.3</span> Create topics for a list of embeddings model</a></li>
</ul></li>
<li><a href="#topics-results" id="toc-topics-results" class="nav-link" data-scroll-target="#topics-results"><span class="header-section-number">7</span> Topics Results</a>
<ul class="collapse">
<li><a href="#representative-documents-of-topics" id="toc-representative-documents-of-topics" class="nav-link" data-scroll-target="#representative-documents-of-topics"><span class="header-section-number">7.1</span> Representative documents of topics</a></li>
<li><a href="#distribution-of-topics" id="toc-distribution-of-topics" class="nav-link" data-scroll-target="#distribution-of-topics"><span class="header-section-number">7.2</span> Distribution of topics</a></li>
<li><a href="#distribution-of-topics-without-outliers" id="toc-distribution-of-topics-without-outliers" class="nav-link" data-scroll-target="#distribution-of-topics-without-outliers"><span class="header-section-number">7.3</span> Distribution of topics without outliers</a></li>
<li><a href="#visualize-how-each-token-contributes-to-a-specific-topic." id="toc-visualize-how-each-token-contributes-to-a-specific-topic." class="nav-link" data-scroll-target="#visualize-how-each-token-contributes-to-a-specific-topic."><span class="header-section-number">7.4</span> Visualize how each token contributes to a specific topic.</a></li>
<li><a href="#some-visualizations-of-the-topics" id="toc-some-visualizations-of-the-topics" class="nav-link" data-scroll-target="#some-visualizations-of-the-topics"><span class="header-section-number">7.5</span> Some visualizations of the topics</a></li>
<li><a href="#wordclouds-of-bertopics" id="toc-wordclouds-of-bertopics" class="nav-link" data-scroll-target="#wordclouds-of-bertopics"><span class="header-section-number">7.6</span> Wordclouds of BERTopics</a></li>
</ul></li>
<li><a href="#bertopic-with-custom-embeddings-model-xlnet" id="toc-bertopic-with-custom-embeddings-model-xlnet" class="nav-link" data-scroll-target="#bertopic-with-custom-embeddings-model-xlnet"><span class="header-section-number">8</span> BERTopic with Custom Embeddings model (xlnet)</a></li>
<li><a href="#word2vec-model-tokenization-with-nltk" id="toc-word2vec-model-tokenization-with-nltk" class="nav-link" data-scroll-target="#word2vec-model-tokenization-with-nltk"><span class="header-section-number">9</span> Word2Vec model (tokenization with nltk)</a>
<ul class="collapse">
<li><a href="#plot-similar-words-for-learning" id="toc-plot-similar-words-for-learning" class="nav-link" data-scroll-target="#plot-similar-words-for-learning"><span class="header-section-number">9.1</span> Plot similar words for “learning”</a></li>
<li><a href="#plot-word2vec-embeddings-in-3d-with-plotly" id="toc-plot-word2vec-embeddings-in-3d-with-plotly" class="nav-link" data-scroll-target="#plot-word2vec-embeddings-in-3d-with-plotly"><span class="header-section-number">9.2</span> Plot Word2Vec embeddings in 3D with plotly</a></li>
<li><a href="#plot-word2vec-embeddings-in-2d-with-plotly" id="toc-plot-word2vec-embeddings-in-2d-with-plotly" class="nav-link" data-scroll-target="#plot-word2vec-embeddings-in-2d-with-plotly"><span class="header-section-number">9.3</span> Plot Word2Vec embeddings in 2D with plotly</a></li>
</ul></li>
<li><a href="#plotting-authors-and-text-based-on-bertopic" id="toc-plotting-authors-and-text-based-on-bertopic" class="nav-link" data-scroll-target="#plotting-authors-and-text-based-on-bertopic"><span class="header-section-number">10</span> Plotting authors and text based on BERTopic</a>
<ul class="collapse">
<li><a href="#d-plot-of-authors-t-sne-bertopic-clustering" id="toc-d-plot-of-authors-t-sne-bertopic-clustering" class="nav-link" data-scroll-target="#d-plot-of-authors-t-sne-bertopic-clustering"><span class="header-section-number">10.1</span> 3D plot of authors (t-SNE + BERTopic clustering)</a></li>
<li><a href="#d-plot-of-authors-t-sne-bertopic-clustering-1" id="toc-d-plot-of-authors-t-sne-bertopic-clustering-1" class="nav-link" data-scroll-target="#d-plot-of-authors-t-sne-bertopic-clustering-1"><span class="header-section-number">10.2</span> 2D plot of authors (t-SNE + BERTopic clustering)</a></li>
</ul></li>
</ul>
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<section id="libraries-r" class="level2" data-number="1">
<h2 data-number="1" class="anchored" data-anchor-id="libraries-r"><span class="header-section-number">1</span> Libraries R</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(gt)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(stringr)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="loading-data-r" class="level2" data-number="2">
<h2 data-number="2" class="anchored" data-anchor-id="loading-data-r"><span class="header-section-number">2</span> Loading data R</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="annotated-cell-2"><pre class="sourceCode r code-annotation-code code-with-copy"><code class="sourceCode r"><span id="annotated-cell-2-1"><a href="#annotated-cell-2-1" aria-hidden="true" tabindex="-1"></a>list_articles <span class="ot"><-</span> <span class="fu">read.csv2</span>(<span class="st">"nlp_full_data_final_18-08-2023.csv"</span>, <span class="at">encoding =</span> <span class="st">"UTF-8"</span>) <span class="sc">%>%</span></span>
<span id="annotated-cell-2-2"><a href="#annotated-cell-2-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(<span class="st">"entry_number"</span> <span class="ot">=</span> <span class="dv">1</span>)</span>
<span id="annotated-cell-2-3"><a href="#annotated-cell-2-3" aria-hidden="true" tabindex="-1"></a>list_references <span class="ot"><-</span> <span class="fu">read.csv2</span>(<span class="st">"nlp_references_final_18-08-2023.csv"</span>, <span class="at">encoding =</span> <span class="st">"UTF-8"</span>) <span class="sc">%>%</span></span>
<span id="annotated-cell-2-4"><a href="#annotated-cell-2-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">rename</span>(<span class="st">"citing_art"</span> <span class="ot">=</span> <span class="dv">1</span>)</span>
<span id="annotated-cell-2-5"><a href="#annotated-cell-2-5" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(list_articles) <span class="ot"><-</span> <span class="fu">gsub</span>(<span class="st">"</span><span class="sc">\\</span><span class="st">.+"</span>, <span class="st">"_"</span>, <span class="fu">colnames</span>(list_articles))</span>
<span id="annotated-cell-2-6"><a href="#annotated-cell-2-6" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(list_articles) <span class="ot"><-</span> <span class="fu">gsub</span>(<span class="st">"^[[:punct:]]+|[[:punct:]]+$"</span>, <span class="st">""</span>, <span class="fu">colnames</span>(list_articles))</span>
<span id="annotated-cell-2-7"><a href="#annotated-cell-2-7" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(list_references) <span class="ot"><-</span> <span class="fu">gsub</span>(<span class="st">"</span><span class="sc">\\</span><span class="st">.+"</span>, <span class="st">"_"</span>, <span class="fu">colnames</span>(list_references))</span>
<span id="annotated-cell-2-8"><a href="#annotated-cell-2-8" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(list_references) <span class="ot"><-</span> <span class="fu">gsub</span>(<span class="st">"^[[:punct:]]+|[[:punct:]]+$"</span>, <span class="st">""</span>, <span class="fu">colnames</span>(list_references))</span>
<span id="annotated-cell-2-9"><a href="#annotated-cell-2-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-2-10"><a href="#annotated-cell-2-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-2-11"><a href="#annotated-cell-2-11" aria-hidden="true" tabindex="-1"></a>data_embeddings <span class="ot"><-</span> list_articles <span class="sc">%>%</span></span>
<span id="annotated-cell-2-12"><a href="#annotated-cell-2-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">distinct</span>(entry_number, <span class="at">.keep_all =</span> <span class="cn">TRUE</span>) <span class="sc">%>%</span></span>
<span id="annotated-cell-2-13"><a href="#annotated-cell-2-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(marketing <span class="sc">==</span> <span class="dv">1</span>) <span class="sc">%>%</span></span>
<span id="annotated-cell-2-14"><a href="#annotated-cell-2-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="st">"year"</span> <span class="ot">=</span> <span class="fu">substr</span>(prism_coverDate, <span class="dv">7</span>, <span class="dv">10</span>)) <span class="sc">%>%</span></span>
<span id="annotated-cell-2-15"><a href="#annotated-cell-2-15" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">keywords =</span> <span class="fu">str_replace_all</span>(authkeywords, <span class="st">"</span><span class="sc">\\</span><span class="st">|"</span>, <span class="st">""</span>)) <span class="sc">%>%</span></span>
<span id="annotated-cell-2-16"><a href="#annotated-cell-2-16" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">keywords =</span> <span class="fu">str_squish</span>(keywords)) <span class="sc">%>%</span></span>
<span id="annotated-cell-2-17"><a href="#annotated-cell-2-17" aria-hidden="true" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="st">"combined_text"</span> <span class="ot">=</span> <span class="fu">paste0</span>(dc_title,<span class="st">". "</span>, dc_description, <span class="st">". "</span>, keywords))</span>
<span id="annotated-cell-2-18"><a href="#annotated-cell-2-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="annotated-cell-2-19"><a href="#annotated-cell-2-19" aria-hidden="true" tabindex="-1"></a><span class="co">#write.csv(data_embeddings,"data_for_embeddings.csv")</span></span>
<span id="annotated-cell-2-20"><a href="#annotated-cell-2-20" aria-hidden="true" tabindex="-1"></a><span class="co">#data_embeddings <- read.csv("data_for_embeddings.csv")</span></span>
<span id="annotated-cell-2-21"><a href="#annotated-cell-2-21" aria-hidden="true" tabindex="-1"></a><span class="co">#embeddings <- read.csv("embeddings_bge.csv")</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="a-glimpse-of-data" class="level2" data-number="3">
<h2 data-number="3" class="anchored" data-anchor-id="a-glimpse-of-data"><span class="header-section-number">3</span> A glimpse of data</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>data_embeddings <span class="sc">%>%</span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">head</span>(<span class="dv">2</span>) <span class="sc">%>%</span></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">select</span>(entry_number, dc_creator, combined_text, year) <span class="sc">%>%</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">gt</span>()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
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<thead>
<tr class="gt_col_headings">
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="entry_number">entry_number</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="dc_creator">dc_creator</th>
<th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="combined_text">combined_text</th>
<th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="year">year</th>
</tr>
</thead>
<tbody class="gt_table_body">
<tr><td headers="entry_number" class="gt_row gt_right">1</td>
<td headers="dc_creator" class="gt_row gt_left">Loupos P.</td>
<td headers="combined_text" class="gt_row gt_left">What reviews foretell about opening weekend box office revenue: the harbinger of failure effect in the movie industry. We empirically investigate the harbinger of failure phenomenon in the motion picture industry by analyzing the pre-release reviews written on movies by film critics. We find that harbingers of failure do exist. Their positive (negative) pre-release movie reviews provide a strong predictive signal that the movie will turn out to be a flop (success). This signal persists even for the top critic category, which usually consists of professional critics, indicating that having expertise in a professional domain does not necessarily lead to correct predictions. Our findings challenge the current belief that positive reviews always help enhance box office revenue and shed new light on the influencer-predictor hypothesis. We further analyze the writing style of harbingers and provide new insights into their personality traits and cognitive biases.. Harbingers of failure Movies Preference heterogeneity Reviews Text analytics</td>
<td headers="year" class="gt_row gt_right">2023</td></tr>
<tr><td headers="entry_number" class="gt_row gt_right">2</td>
<td headers="dc_creator" class="gt_row gt_left">Krefeld-Schwalb A.</td>
<td headers="combined_text" class="gt_row gt_left">Tighter nets for smaller fishes? Mapping the development of statistical practices in consumer research between 2008 and 2020. During the last decade, confidence in many social sciences, including consumer research, has been undermined by doubts about the replicability of empirical research findings. These doubts have led to increased calls to improve research practices and adopt new measures to increase the replicability of published work from various stakeholders such as funding agencies, journals, and scholars themselves. Despite these demands, it is unclear to which the research published in the leading consumer research journals has adhered to these calls for change. This article provides the first systematic empirical analysis of this question by surveying three crucial statistics of published consumer research over time: sample sizes, effect sizes, and the distribution of published p values. The authors compile a hand-coded sample of N = 258 articles published between 2008 and 2020 in the Journal of Consumer Psychology, the Journal of Consumer Research, and the Journal of Marketing Research. An automated text analysis across all publications in these three journals corroborates the representativeness of the hand-coded sample. Results reveal a substantial increase in sample sizes above and beyond the use of online samples along with a decrease in reported effect sizes. Effect and samples sizes are highly correlated which at least partially explains the reduction in reported effect sizes.. Experimental research methods False-positive results Review</td>
<td headers="year" class="gt_row gt_right">2023</td></tr>
</tbody>
</table>
</div>
</div>
</div>
</section>
<section id="python-libraries-and-loading-data" class="level2" data-number="4">
<h2 data-number="4" class="anchored" data-anchor-id="python-libraries-and-loading-data"><span class="header-section-number">4</span> Python libraries and loading data</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> warnings</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>warnings.filterwarnings(<span class="st">"ignore"</span>, message<span class="op">=</span><span class="st">".*The 'nopython' keyword.*"</span>)</span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> matplotlib.pyplot <span class="im">as</span> plt</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> nltk</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> numpy <span class="im">as</span> np</span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> os</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> palettable</span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.express <span class="im">as</span> px</span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> plotly.io <span class="im">as</span> pio</span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> string</span>
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> stylecloud</span>
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> time</span>
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> torch</span>
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> umap.umap_ <span class="im">as</span> umap</span>
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> bertopic <span class="im">import</span> BERTopic</span>
<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> bertopic.vectorizers <span class="im">import</span> ClassTfidfTransformer</span>
<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> bertopic.representation <span class="im">import</span> MaximalMarginalRelevance</span>
<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> gensim.models <span class="im">import</span> Word2Vec</span>
<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> nltk.corpus <span class="im">import</span> stopwords</span>
<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> nltk.tokenize <span class="im">import</span> word_tokenize</span>
<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> palettable <span class="im">import</span> colorbrewer</span>
<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sentence_transformers <span class="im">import</span> SentenceTransformer, util</span>
<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.cluster <span class="im">import</span> DBSCAN</span>
<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.decomposition <span class="im">import</span> PCA</span>
<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.feature_extraction.text <span class="im">import</span> CountVectorizer</span>
<span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.manifold <span class="im">import</span> TSNE</span>
<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> sklearn.metrics <span class="im">import</span> davies_bouldin_score, silhouette_score, silhouette_samples</span>
<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tabulate <span class="im">import</span> tabulate</span>
<span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> tqdm <span class="im">import</span> tqdm</span>
<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> transformers <span class="im">import</span> XLNetTokenizer, XLNetModel</span>
<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> yellowbrick.cluster <span class="im">import</span> SilhouetteVisualizer</span>
<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> wordcloud <span class="im">import</span> WordCloud</span>
<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a>df <span class="op">=</span> pd.read_csv(<span class="st">"data_for_embeddings.csv"</span>)</span>
<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a><span class="co">#df['title_abstract'] = df['dc_title'].astype(str) + '. ' + df['dc_description'].astype(str)</span></span>
<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a>docs_marketing <span class="op">=</span> df[<span class="st">"combined_text"</span>].tolist()</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="cuda-status-and-device-info" class="level2" data-number="5">
<h2 data-number="5" class="anchored" data-anchor-id="cuda-status-and-device-info"><span class="header-section-number">5</span> CUDA Status and Device Info</h2>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Is CUDA supported by this system? </span><span class="sc">{</span>torch<span class="sc">.</span>cuda<span class="sc">.</span>is_available()<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>Is CUDA supported by this system? True</code></pre>
</div>
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"CUDA version: </span><span class="sc">{</span>torch<span class="sc">.</span>version<span class="sc">.</span>cuda<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>CUDA version: 12.1</code></pre>
</div>
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Storing ID of the current CUDA device</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>cuda_id <span class="op">=</span> torch.cuda.current_device()</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"ID of the current CUDA device: </span><span class="sc">{</span>cuda_id<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>ID of the current CUDA device: 0</code></pre>
</div>
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Name of the current CUDA device: </span><span class="sc">{</span>torch<span class="sc">.</span>cuda<span class="sc">.</span>get_device_name(cuda_id)<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output cell-output-stdout">
<pre><code>Name of the current CUDA device: NVIDIA GeForce RTX 3070</code></pre>
</div>
</div>
</section>
<section id="detect-interpretable-topics-with-bertopic" class="level2" data-number="6">
<h2 data-number="6" class="anchored" data-anchor-id="detect-interpretable-topics-with-bertopic"><span class="header-section-number">6</span> Detect interpretable topics with BERTopic</h2>
<div class="callout callout-style-default callout-note callout-titled">
<div class="callout-header d-flex align-content-center">
<div class="callout-icon-container">
<i class="callout-icon"></i>
</div>
<div class="callout-title-container flex-fill">
Note
</div>
</div>
<div class="callout-body-container callout-body">
<p>We use a CountVectorizer which enables us to specify the range of the ngram we want in our topic model. We can use it before or after the topic modelling (update topic).<br>
Here we use it before the topic modelling to exclude english stopwords, but after the embeddings process so that the foundation provided by stopwords in sentences is preserved in context.</p>
</div>
</div>
<section id="function-to-create-bertopics-with-custom-embeddings" class="level3" data-number="6.1">
<h3 data-number="6.1" class="anchored" data-anchor-id="function-to-create-bertopics-with-custom-embeddings"><span class="header-section-number">6.1</span> Function to create BERTopics with custom embeddings</h3>
<p>The aim of this function is to swiftly create various BERTopic experiments while maintaining the same parameters, except for the choice of the embedding model. This enables the generation of distinct BERTopic results, facilitating meaningful comparisons among them.</p>
<p>Some explanations:</p>
<table class="table">
<thead>
<tr class="header">
<th>Parameter name</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>docs</td>
<td>The documents we want to analyze (list).</td>
</tr>
<tr class="even">
<td>embeddings_model</td>
<td>Specifies the embeddings model we want to load and use.</td>
</tr>
<tr class="odd">
<td>min_topic_size</td>
<td>It is used to specify what the minimum size of a topic can be. See <a href="https://maartengr.github.io/BERTopic/getting_started/parameter%20tuning/parametertuning.html#min_topic_size" title="min_topic_size">BERTopic documentation</a>.</td>
</tr>
<tr class="even">
<td>nr_topics</td>
<td>The number of topics we want to reduce our results to. See <a href="https://maartengr.github.io/BERTopic/getting_started/parameter%20tuning/parametertuning.html#nr_topics" title="min_topic_size">BERTopic documentation</a>.</td>
</tr>
</tbody>
</table>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> create_bertopic(docs, embeddings_model, min_topic_size, nr_topics):</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a> <span class="co"># initialize a count-based tf-idf transformer</span></span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a> ctfidf_model <span class="op">=</span> ClassTfidfTransformer(reduce_frequent_words<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a> <span class="co"># initialize a sentence transformer model for embeddings</span></span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a> sentence_model <span class="op">=</span> SentenceTransformer(embeddings_model, device<span class="op">=</span><span class="st">'cuda'</span>)</span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a> <span class="co"># generate embeddings for the input documents</span></span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a> embeddings <span class="op">=</span> sentence_model.encode(docs, show_progress_bar<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a> <span class="co"># create the representation model</span></span>
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a> <span class="co">#representation_model = MaximalMarginalRelevance(diversity=1)</span></span>
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a> <span class="co"># create a bertopic model with specified parameters</span></span>
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a> topic_model <span class="op">=</span> BERTopic(</span>
<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a> ctfidf_model<span class="op">=</span>ctfidf_model,</span>
<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a> calculate_probabilities<span class="op">=</span><span class="va">True</span>,</span>
<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a> verbose<span class="op">=</span><span class="va">True</span>,</span>
<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a> min_topic_size<span class="op">=</span>min_topic_size,</span>
<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a> nr_topics<span class="op">=</span>nr_topics,</span>
<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a> top_n_words<span class="op">=</span><span class="dv">20</span></span>
<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a> <span class="co">#representation_model=representation_model</span></span>
<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a> <span class="co"># fit the bertopic model to the input documents and embeddings</span></span>
<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a> topics, probs <span class="op">=</span> topic_model.fit_transform(docs, embeddings)</span>
<span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-29"><a href="#cb12-29" aria-hidden="true" tabindex="-1"></a> <span class="co"># update the vectorizer model used by bertopic</span></span>
<span id="cb12-30"><a href="#cb12-30" aria-hidden="true" tabindex="-1"></a> <span class="co"># `min_df` is the minimum document frequency for terms (words or n-grams) in the CountVectorizer.</span></span>
<span id="cb12-31"><a href="#cb12-31" aria-hidden="true" tabindex="-1"></a> updated_vectorizer_model <span class="op">=</span> CountVectorizer(stop_words<span class="op">=</span><span class="st">"english"</span>, ngram_range<span class="op">=</span>(<span class="dv">1</span>, <span class="dv">3</span>), min_df<span class="op">=</span><span class="dv">3</span>)</span>
<span id="cb12-32"><a href="#cb12-32" aria-hidden="true" tabindex="-1"></a> topic_model.update_topics(docs, vectorizer_model<span class="op">=</span>updated_vectorizer_model)</span>
<span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a> <span class="co"># return the trained bertopic model</span></span>
<span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> topic_model</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="function-to-visualize-bertopics" class="level3" data-number="6.2">
<h3 data-number="6.2" class="anchored" data-anchor-id="function-to-visualize-bertopics"><span class="header-section-number">6.2</span> Function to visualize BERTopics</h3>
<p>Creates a folder in Images/ with the model_name input with various plots in html files.</p>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> generate_topics_table(topic_model):</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a> <span class="co"># get topic information from the model</span></span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a> topics_info <span class="op">=</span> topic_model.get_topic_info()</span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a> <span class="co"># check if topics_info is empty or None</span></span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> topics_info <span class="kw">is</span> <span class="va">None</span> <span class="kw">or</span> topics_info.empty:</span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="st">"No topics found."</span></span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a> <span class="co"># convert the data into a list</span></span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a> data_as_list <span class="op">=</span> topics_info.values.tolist()</span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a> <span class="co"># get column names as headers</span></span>
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a> headers <span class="op">=</span> topics_info.columns.tolist()</span>
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a> <span class="co"># generate the table in HTML format</span></span>
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a> table <span class="op">=</span> tabulate(data_as_list, headers, tablefmt<span class="op">=</span><span class="st">'html'</span>)</span>
<span id="cb13-17"><a href="#cb13-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-18"><a href="#cb13-18" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> table</span>
<span id="cb13-19"><a href="#cb13-19" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb13-21"><a href="#cb13-21" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-22"><a href="#cb13-22" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> visualize_bertopic(topic_model, model_name, nr_topics):</span>
<span id="cb13-23"><a href="#cb13-23" aria-hidden="true" tabindex="-1"></a> <span class="co"># create the "images" folder if it doesn't exist already</span></span>
<span id="cb13-24"><a href="#cb13-24" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> <span class="kw">not</span> os.path.exists(<span class="st">"images"</span>):</span>
<span id="cb13-25"><a href="#cb13-25" aria-hidden="true" tabindex="-1"></a> os.makedirs(<span class="st">"images"</span>)</span>
<span id="cb13-26"><a href="#cb13-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-27"><a href="#cb13-27" aria-hidden="true" tabindex="-1"></a> <span class="co"># create a subfolder for the specific topic model</span></span>
<span id="cb13-28"><a href="#cb13-28" aria-hidden="true" tabindex="-1"></a> model_folder <span class="op">=</span> os.path.join(<span class="st">"images"</span>, model_name<span class="op">+</span><span class="st">"-"</span><span class="op">+</span><span class="bu">str</span>(nr_topics)<span class="op">+</span><span class="st">"topics"</span>)</span>
<span id="cb13-29"><a href="#cb13-29" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb13-30"><a href="#cb13-30" aria-hidden="true" tabindex="-1"></a> <span class="co"># create the model folder if it doesn't exist already</span></span>
<span id="cb13-31"><a href="#cb13-31" aria-hidden="true" tabindex="-1"></a> <span class="cf">if</span> <span class="kw">not</span> os.path.exists(model_folder):</span>
<span id="cb13-32"><a href="#cb13-32" aria-hidden="true" tabindex="-1"></a> os.makedirs(model_folder)</span>
<span id="cb13-33"><a href="#cb13-33" aria-hidden="true" tabindex="-1"></a> <span class="cf">else</span>:</span>
<span id="cb13-34"><a href="#cb13-34" aria-hidden="true" tabindex="-1"></a> <span class="co"># delete existing files in the model folder if it exists</span></span>
<span id="cb13-35"><a href="#cb13-35" aria-hidden="true" tabindex="-1"></a> <span class="cf">for</span> <span class="bu">file</span> <span class="kw">in</span> os.listdir(model_folder):</span>
<span id="cb13-36"><a href="#cb13-36" aria-hidden="true" tabindex="-1"></a> os.remove(os.path.join(model_folder, <span class="bu">file</span>))</span>
<span id="cb13-37"><a href="#cb13-37" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-38"><a href="#cb13-38" aria-hidden="true" tabindex="-1"></a> <span class="co"># generate topics information table</span></span>
<span id="cb13-39"><a href="#cb13-39" aria-hidden="true" tabindex="-1"></a> topics_table <span class="op">=</span> generate_topics_table(topic_model)</span>
<span id="cb13-40"><a href="#cb13-40" aria-hidden="true" tabindex="-1"></a> <span class="cf">with</span> <span class="bu">open</span>(os.path.join(model_folder, <span class="st">'table_topics.html'</span>), <span class="st">'w'</span>) <span class="im">as</span> f:</span>
<span id="cb13-41"><a href="#cb13-41" aria-hidden="true" tabindex="-1"></a> f.write(topics_table)</span>
<span id="cb13-42"><a href="#cb13-42" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb13-43"><a href="#cb13-43" aria-hidden="true" tabindex="-1"></a> <span class="co"># visualize topics</span></span>
<span id="cb13-44"><a href="#cb13-44" aria-hidden="true" tabindex="-1"></a> fig_topics <span class="op">=</span> topic_model.visualize_topics()</span>
<span id="cb13-45"><a href="#cb13-45" aria-hidden="true" tabindex="-1"></a> fig_topics.write_html(os.path.join(model_folder, <span class="st">"topicsinfo.html"</span>))</span>
<span id="cb13-46"><a href="#cb13-46" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-47"><a href="#cb13-47" aria-hidden="true" tabindex="-1"></a> <span class="co"># visualize hierarchy</span></span>
<span id="cb13-48"><a href="#cb13-48" aria-hidden="true" tabindex="-1"></a> fig_hierarchy <span class="op">=</span> topic_model.visualize_hierarchy()</span>
<span id="cb13-49"><a href="#cb13-49" aria-hidden="true" tabindex="-1"></a> fig_hierarchy.write_html(os.path.join(model_folder, <span class="st">"hierarchy.html"</span>))</span>
<span id="cb13-50"><a href="#cb13-50" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-51"><a href="#cb13-51" aria-hidden="true" tabindex="-1"></a> <span class="co"># visualize hierarchical topics</span></span>
<span id="cb13-52"><a href="#cb13-52" aria-hidden="true" tabindex="-1"></a> hierarchical_topics <span class="op">=</span> topic_model.hierarchical_topics(docs_marketing)</span>
<span id="cb13-53"><a href="#cb13-53" aria-hidden="true" tabindex="-1"></a> fig_hierarchical_topics <span class="op">=</span> topic_model.visualize_hierarchy(hierarchical_topics<span class="op">=</span>hierarchical_topics)</span>
<span id="cb13-54"><a href="#cb13-54" aria-hidden="true" tabindex="-1"></a> fig_hierarchical_topics.write_html(os.path.join(model_folder, <span class="st">"hierarchical.html"</span>))</span>
<span id="cb13-55"><a href="#cb13-55" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-56"><a href="#cb13-56" aria-hidden="true" tabindex="-1"></a> <span class="co"># visualize the bar chart</span></span>
<span id="cb13-57"><a href="#cb13-57" aria-hidden="true" tabindex="-1"></a> fig_barchart <span class="op">=</span> topic_model.visualize_barchart(width<span class="op">=</span><span class="dv">300</span>, height<span class="op">=</span><span class="dv">300</span>, n_words<span class="op">=</span><span class="dv">10</span>, topics<span class="op">=</span><span class="va">None</span>, top_n_topics<span class="op">=</span><span class="dv">20</span>)</span>
<span id="cb13-58"><a href="#cb13-58" aria-hidden="true" tabindex="-1"></a> fig_barchart.write_html(os.path.join(model_folder, <span class="st">"barchart.html"</span>))</span>
<span id="cb13-59"><a href="#cb13-59" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-60"><a href="#cb13-60" aria-hidden="true" tabindex="-1"></a> <span class="co"># visualize the heatmap</span></span>
<span id="cb13-61"><a href="#cb13-61" aria-hidden="true" tabindex="-1"></a> fig_heatmap <span class="op">=</span> topic_model.visualize_heatmap()</span>
<span id="cb13-62"><a href="#cb13-62" aria-hidden="true" tabindex="-1"></a> fig_heatmap.write_html(os.path.join(model_folder, <span class="st">"heatmap.html"</span>))</span>
<span id="cb13-63"><a href="#cb13-63" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb13-64"><a href="#cb13-64" aria-hidden="true" tabindex="-1"></a> <span class="co"># topics over time</span></span>
<span id="cb13-65"><a href="#cb13-65" aria-hidden="true" tabindex="-1"></a> years <span class="op">=</span> df[<span class="st">'year'</span>].to_list()</span>
<span id="cb13-66"><a href="#cb13-66" aria-hidden="true" tabindex="-1"></a> topics_over_time <span class="op">=</span> topic_model.topics_over_time(docs_marketing, years)</span>
<span id="cb13-67"><a href="#cb13-67" aria-hidden="true" tabindex="-1"></a> fig_topics_over_time <span class="op">=</span> topic_model.visualize_topics_over_time(topics_over_time, top_n_topics<span class="op">=</span><span class="dv">20</span>, normalize_frequency<span class="op">=</span><span class="va">True</span>)</span>
<span id="cb13-68"><a href="#cb13-68" aria-hidden="true" tabindex="-1"></a> fig_topics_over_time.write_html(os.path.join(model_folder, <span class="st">"topicsovertime.html"</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="create-topics-for-a-list-of-embeddings-model" class="level3" data-number="6.3">
<h3 data-number="6.3" class="anchored" data-anchor-id="create-topics-for-a-list-of-embeddings-model"><span class="header-section-number">6.3</span> Create topics for a list of embeddings model</h3>
<p>We must specify the number of topics we want to create (<code>nbtopics</code>) and the minimum number of documents to form a topic (<code>nbmintopicsize</code>).</p>
<div class="cell">
<details>
<summary>Code</summary>
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co">#09/26/2023 -----------------------------------------</span></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="co">#to-do clean code : embeddings are charged twice for viz purposes (document viz) because it is loaded a first time in the create_bertopic function and again in the loop (can't use just the model name in visualize_bertopic function.)</span></span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a><span class="co">#---------------------------------------------------</span></span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a><span class="co"># list of embeddings models</span></span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>list_embeddings <span class="op">=</span> [<span class="st">"all-mpnet-base-v2"</span>]</span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a><span class="co">#list_embeddings = ["all-mpnet-base-v2","multi-qa-mpnet-base-dot-v1","all-roberta-large-v1","all-MiniLM-L12-v2"]</span></span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a><span class="co"># create a list to store model information</span></span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a>table_data <span class="op">=</span> []</span>
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a>topic_models <span class="op">=</span> {}</span>
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a><span class="co">#nbtopics is the number of topics we want to create/reduce to</span></span>
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a><span class="co">#nbmintopicsize is the minimum number of documents to form a topic</span></span>
<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a>nbtopics <span class="op">=</span> <span class="dv">17</span></span>
<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a>nbmintopicsize <span class="op">=</span> <span class="dv">5</span></span>
<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a><span class="co"># loop through the list of embeddings models and create topic_model + viz in images</span></span>
<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a><span class="cf">for</span> embeddings_model <span class="kw">in</span> list_embeddings:</span>
<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="ss">f"</span><span class="ch">\n</span><span class="ss">Creating BERTopics with the </span><span class="sc">{</span>embeddings_model<span class="sc">}</span><span class="ss"> Sentence-Transformers pretrained model."</span>)</span>
<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a> topic_model <span class="op">=</span> create_bertopic(docs_marketing, embeddings_model, nbmintopicsize, nbtopics)</span>
<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a> <span class="bu">print</span>(<span class="ss">f"</span><span class="ch">\n</span><span class="ss">Creating BERTopic visualizations in the `images</span><span class="ch">\\</span><span class="sc">{</span>embeddings_model<span class="sc">}</span><span class="ss">-</span><span class="sc">{</span>nbtopics<span class="sc">}</span><span class="ss">topics` folder."</span>)</span>
<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a> visualize_bertopic(topic_model, embeddings_model, nbtopics)</span>
<span id="cb14-24"><a href="#cb14-24" aria-hidden="true" tabindex="-1"></a> chargedmodel <span class="op">=</span> SentenceTransformer(embeddings_model, device<span class="op">=</span><span class="st">'cuda'</span>)</span>
<span id="cb14-25"><a href="#cb14-25" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb14-26"><a href="#cb14-26" aria-hidden="true" tabindex="-1"></a> <span class="co"># visualize the documents</span></span>
<span id="cb14-27"><a href="#cb14-27" aria-hidden="true" tabindex="-1"></a> model_folder <span class="op">=</span> os.path.join(<span class="st">"images"</span>, embeddings_model<span class="op">+</span><span class="st">"-"</span><span class="op">+</span><span class="bu">str</span>(nbtopics)<span class="op">+</span><span class="st">"topics"</span>)</span>
<span id="cb14-28"><a href="#cb14-28" aria-hidden="true" tabindex="-1"></a> embeddings <span class="op">=</span> chargedmodel.encode(docs_marketing, show_progress_bar<span class="op">=</span><span class="va">False</span>)</span>
<span id="cb14-29"><a href="#cb14-29" aria-hidden="true" tabindex="-1"></a> fig_documents <span class="op">=</span> topic_model.visualize_documents(docs_marketing, embeddings<span class="op">=</span>embeddings)</span>
<span id="cb14-30"><a href="#cb14-30" aria-hidden="true" tabindex="-1"></a> fig_documents.write_html(os.path.join(model_folder, <span class="st">"documents_topics.html"</span>))</span>
<span id="cb14-31"><a href="#cb14-31" aria-hidden="true" tabindex="-1"></a> </span>
<span id="cb14-32"><a href="#cb14-32" aria-hidden="true" tabindex="-1"></a> <span class="co"># to summarize embeddings' models</span></span>
<span id="cb14-33"><a href="#cb14-33" aria-hidden="true" tabindex="-1"></a> dimensions <span class="op">=</span> chargedmodel.get_sentence_embedding_dimension()</span>
<span id="cb14-34"><a href="#cb14-34" aria-hidden="true" tabindex="-1"></a> max_tokens <span class="op">=</span> chargedmodel.max_seq_length</span>