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<title>Chapter 8 Topic modeling | Natural Language Processing with R</title>
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<li><a href="./">NLP with R</a></li>
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<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> Introduction</a></li>
<li class="chapter" data-level="2" data-path="text-processing.html"><a href="text-processing.html"><i class="fa fa-check"></i><b>2</b> Text processing</a><ul>
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<li class="chapter" data-level="7" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html"><i class="fa fa-check"></i><b>7</b> Word and document frequency (TF-IDF)</a><ul>
<li class="chapter" data-level="7.1" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html#term-frequency-application"><i class="fa fa-check"></i><b>7.1</b> Term frequency application</a></li>
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<li class="chapter" data-level="8" data-path="topic-modeling.html"><a href="topic-modeling.html"><i class="fa fa-check"></i><b>8</b> Topic modeling</a><ul>
<li class="chapter" data-level="8.1" data-path="topic-modeling.html"><a href="topic-modeling.html#latent-dirichlet-allocation"><i class="fa fa-check"></i><b>8.1</b> Latent Dirichlet allocation</a></li>
<li class="chapter" data-level="8.2" data-path="topic-modeling.html"><a href="topic-modeling.html#document-topic-probabilities"><i class="fa fa-check"></i><b>8.2</b> Document-topic probabilities</a></li>
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<li class="chapter" data-level="10.1" data-path="document-term-matrix.html"><a href="document-term-matrix.html#converting-dtm-into-dataframe"><i class="fa fa-check"></i><b>10.1</b> COnverting DTM into dataframe</a></li>
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<div id="topic-modeling" class="section level1">
<h1><span class="header-section-number">Chapter 8</span> Topic modeling</h1>
<p>Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents.</p>
<div id="latent-dirichlet-allocation" class="section level2">
<h2><span class="header-section-number">8.1</span> Latent Dirichlet allocation</h2>
<p>Latent Dirichlet allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It treats each document as a mixture of topics, and each topic as a mixture of words. LDA is a mathematical method for finding the mixture of words associated with each topic and the mixture of topics that describes each document.</p>
<p>Here is an example of applying LDA model with 2 topics as parameter:</p>
<div class="sourceCode" id="cb143"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb143-1" title="1"><span class="kw">library</span>(topicmodels)</a>
<a class="sourceLine" id="cb143-2" title="2"></a>
<a class="sourceLine" id="cb143-3" title="3"><span class="co"># load data</span></a>
<a class="sourceLine" id="cb143-4" title="4"><span class="kw">data</span>(<span class="st">"AssociatedPress"</span>)</a>
<a class="sourceLine" id="cb143-5" title="5">AssociatedPress</a></code></pre></div>
<pre><code>## <<DocumentTermMatrix (documents: 2246, terms: 10473)>>
## Non-/sparse entries: 302031/23220327
## Sparsity : 99%
## Maximal term length: 18
## Weighting : term frequency (tf)</code></pre>
<div class="sourceCode" id="cb145"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb145-1" title="1"><span class="co"># fitting LDA model with 2 topics</span></a>
<a class="sourceLine" id="cb145-2" title="2">ap_lda =<span class="st"> </span><span class="kw">LDA</span>(AssociatedPress, <span class="dt">k=</span><span class="dv">2</span>, <span class="dt">control =</span> <span class="kw">list</span>(<span class="dt">seed =</span> <span class="dv">1234</span>))</a>
<a class="sourceLine" id="cb145-3" title="3">ap_lda</a></code></pre></div>
<pre><code>## A LDA_VEM topic model with 2 topics.</code></pre>
<p>Now we can extract the per-topic-per-word probabilities from the model</p>
<div class="sourceCode" id="cb147"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb147-1" title="1"><span class="kw">library</span>(tidytext)</a>
<a class="sourceLine" id="cb147-2" title="2"></a>
<a class="sourceLine" id="cb147-3" title="3">ap_topics =<span class="st"> </span><span class="kw">tidy</span>(ap_lda, <span class="dt">matrix =</span> <span class="st">"beta"</span>)</a>
<a class="sourceLine" id="cb147-4" title="4">ap_topics</a></code></pre></div>
<pre><code>## # A tibble: 20,946 x 3
## topic term beta
## <int> <chr> <dbl>
## 1 1 aaron 1.69e-12
## 2 2 aaron 3.90e- 5
## 3 1 abandon 2.65e- 5
## 4 2 abandon 3.99e- 5
## 5 1 abandoned 1.39e- 4
## 6 2 abandoned 5.88e- 5
## 7 1 abandoning 2.45e-33
## 8 2 abandoning 2.34e- 5
## 9 1 abbott 2.13e- 6
## 10 2 abbott 2.97e- 5
## # ... with 20,936 more rows</code></pre>
<p>The resulting dataframe present the probability of each term to be generated from the different topics. For example the term “abandoned” has a probability of <span class="math inline">\(1.39 \times 10^{-4}\)</span> of beng generated from topic 1 and a probability of<span class="math inline">\(5.88 \times 10^{-5}\)</span> for being generated from topic 2.</p>
<p>Let’s find the 10 terms that are most common within each topic.</p>
<div class="sourceCode" id="cb149"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb149-1" title="1"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb149-2" title="2"><span class="kw">library</span>(dplyr)</a>
<a class="sourceLine" id="cb149-3" title="3"></a>
<a class="sourceLine" id="cb149-4" title="4">ap_top_terms <-<span class="st"> </span>ap_topics <span class="op">%>%</span></a>
<a class="sourceLine" id="cb149-5" title="5"><span class="st"> </span><span class="kw">group_by</span>(topic) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb149-6" title="6"><span class="st"> </span><span class="kw">top_n</span>(<span class="dv">10</span>, beta) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb149-7" title="7"><span class="st"> </span><span class="kw">ungroup</span>() <span class="op">%>%</span></a>
<a class="sourceLine" id="cb149-8" title="8"><span class="st"> </span><span class="kw">arrange</span>(topic, <span class="op">-</span>beta)</a>
<a class="sourceLine" id="cb149-9" title="9"></a>
<a class="sourceLine" id="cb149-10" title="10">ap_top_terms <span class="op">%>%</span></a>
<a class="sourceLine" id="cb149-11" title="11"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">term =</span> <span class="kw">reorder_within</span>(term, beta, topic)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb149-12" title="12"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(term, beta, <span class="dt">fill =</span> <span class="kw">factor</span>(topic))) <span class="op">+</span></a>
<a class="sourceLine" id="cb149-13" title="13"><span class="st"> </span><span class="kw">geom_col</span>(<span class="dt">show.legend =</span> <span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb149-14" title="14"><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>topic, <span class="dt">scales =</span> <span class="st">"free"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb149-15" title="15"><span class="st"> </span><span class="kw">coord_flip</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb149-16" title="16"><span class="st"> </span><span class="kw">scale_x_reordered</span>()</a></code></pre></div>
<p><img src="NLP-book_files/figure-html/unnamed-chunk-20-1.png" width="672" /></p>
<p>We can interpret the result as a first topic related to finanial news (“precent’,”million“,”company“) and a second topic related to political news (”president“,”government“,”states").</p>
</div>
<div id="document-topic-probabilities" class="section level2">
<h2><span class="header-section-number">8.2</span> Document-topic probabilities</h2>
<p>Besides estimating each topic as a mixture of words, LDA also models each document as a mixture of topics. For examining per-document-per-topic probabilities, we use the “gamma” metric.</p>
<div class="sourceCode" id="cb150"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb150-1" title="1">ap_documents <-<span class="st"> </span><span class="kw">tidy</span>(ap_lda, <span class="dt">matrix =</span> <span class="st">"gamma"</span>)</a>
<a class="sourceLine" id="cb150-2" title="2">ap_documents</a></code></pre></div>
<pre><code>## # A tibble: 4,492 x 3
## document topic gamma
## <int> <int> <dbl>
## 1 1 1 0.248
## 2 2 1 0.362
## 3 3 1 0.527
## 4 4 1 0.357
## 5 5 1 0.181
## 6 6 1 0.000588
## 7 7 1 0.773
## 8 8 1 0.00445
## 9 9 1 0.967
## 10 10 1 0.147
## # ... with 4,482 more rows</code></pre>
<p>Each of these values is an estimated proportion of words from that document that are generated from that topic. For example, the model estimates that only about 25% of the words in document 1 were generated from topic 1.</p>
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