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<div id="document-term-matrix" class="section level1">
<h1><span class="header-section-number">Chapter 10</span> Document-term matrix</h1>
<p>A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix:</p>
<ul>
<li>Rows correspond to documents in the collection and</li>
<li>Columns correspond to terms</li>
<li>Values contain the number of appearances of terms in the specified documents</li>
</ul>
<div id="converting-dtm-into-dataframe" class="section level2">
<h2><span class="header-section-number">10.1</span> COnverting DTM into dataframe</h2>
<p>We will see how to transform a document-term matrix into a dataframe. We can find examples of DTM data by loading <code>topicmodels</code> package.</p>
<div class="sourceCode" id="cb168"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb168-1" title="1"><span class="kw">library</span>(tm)</a>
<a class="sourceLine" id="cb168-2" title="2"><span class="kw">library</span>(topicmodels)</a>
<a class="sourceLine" id="cb168-3" title="3"><span class="kw">library</span>(quanteda)</a>
<a class="sourceLine" id="cb168-4" title="4"></a>
<a class="sourceLine" id="cb168-5" title="5"><span class="kw">data</span>(<span class="st">"AssociatedPress"</span>, <span class="dt">package =</span> <span class="st">"topicmodels"</span>)</a>
<a class="sourceLine" id="cb168-6" title="6">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>
<p>The loaded dataset contains 2246 documents and 10473 distinct terms. We notice that this DTM is 99% sparse (99% of document-word paris are zero). We can get the terms using <code>Terms()</code> function.</p>
<div class="sourceCode" id="cb170"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb170-1" title="1">terms =<span class="st"> </span><span class="kw">Terms</span>(AssociatedPress)</a>
<a class="sourceLine" id="cb170-2" title="2"><span class="kw">head</span>(terms)</a></code></pre></div>
<pre><code>## [1] "aaron" "abandon" "abandoned" "abandoning" "abbott"
## [6] "abboud"</code></pre>
<p>In order to analyze the data, we should transform it inot dataframe. We can use <code>tidy()</code> function to do that.</p>
<div class="sourceCode" id="cb172"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb172-1" title="1">ap_td =<span class="st"> </span><span class="kw">tidy</span>(AssociatedPress)</a>
<a class="sourceLine" id="cb172-2" title="2">ap_td</a></code></pre></div>
<pre><code>## # A tibble: 302,031 x 3
## document term count
## <int> <chr> <dbl>
## 1 1 adding 1
## 2 1 adult 2
## 3 1 ago 1
## 4 1 alcohol 1
## 5 1 allegedly 1
## 6 1 allen 1
## 7 1 apparently 2
## 8 1 appeared 1
## 9 1 arrested 1
## 10 1 assault 1
## # ... with 302,021 more rows</code></pre>
<p>Once we have the data in a dataframe format, we can perform some analysis. Here is an example of applying sentiment analysis to evaluate the negative and positive terms in the collection.</p>
<div class="sourceCode" id="cb174"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb174-1" title="1"><span class="co"># using "bing" database to attribute negative/positive attribute to terms</span></a>
<a class="sourceLine" id="cb174-2" title="2">ap_sentiments =<span class="st"> </span>ap_td <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb174-3" title="3"><span class="st"> </span><span class="kw">inner_join</span>(<span class="kw">get_sentiments</span>(<span class="st">"bing"</span>), <span class="dt">by =</span> <span class="kw">c</span>(<span class="dt">term =</span> <span class="st">"word"</span>))</a>
<a class="sourceLine" id="cb174-4" title="4">ap_sentiments</a></code></pre></div>
<pre><code>## # A tibble: 30,094 x 4
## document term count sentiment
## <int> <chr> <dbl> <chr>
## 1 1 assault 1 negative
## 2 1 complex 1 negative
## 3 1 death 1 negative
## 4 1 died 1 negative
## 5 1 good 2 positive
## 6 1 illness 1 negative
## 7 1 killed 2 negative
## 8 1 like 2 positive
## 9 1 liked 1 positive
## 10 1 miracle 1 positive
## # ... with 30,084 more rows</code></pre>
<div class="sourceCode" id="cb176"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb176-1" title="1"><span class="co"># plot the results</span></a>
<a class="sourceLine" id="cb176-2" title="2"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb176-3" title="3"></a>
<a class="sourceLine" id="cb176-4" title="4">ap_sentiments <span class="op">%>%</span></a>
<a class="sourceLine" id="cb176-5" title="5"><span class="st"> </span><span class="kw">count</span>(sentiment, term, <span class="dt">wt =</span> count) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb176-6" title="6"><span class="st"> </span><span class="kw">ungroup</span>() <span class="op">%>%</span></a>
<a class="sourceLine" id="cb176-7" title="7"><span class="st"> </span><span class="kw">filter</span>(n <span class="op">>=</span><span class="st"> </span><span class="dv">200</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb176-8" title="8"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">n =</span> <span class="kw">ifelse</span>(sentiment <span class="op">==</span><span class="st"> "negative"</span>, <span class="op">-</span>n, n)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb176-9" title="9"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">term =</span> <span class="kw">reorder</span>(term, n)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb176-10" title="10"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(term, n, <span class="dt">fill =</span> sentiment)) <span class="op">+</span></a>
<a class="sourceLine" id="cb176-11" title="11"><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"identity"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb176-12" title="12"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Contribution to sentiment"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb176-13" title="13"><span class="st"> </span><span class="kw">coord_flip</span>()</a></code></pre></div>
<p><img src="NLP-book_files/figure-html/unnamed-chunk-30-1.png" width="672" /></p>
</div>
<div id="generating-document-term-matrix" class="section level2">
<h2><span class="header-section-number">10.2</span> Generating Document-term matrix</h2>
<p>Some algorithms may need document-term matrix as input. The <code>cast_dtm</code> function enable the generation of DTM structure from a dataframe.</p>
<div class="sourceCode" id="cb177"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb177-1" title="1">ap_td <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb177-2" title="2"><span class="st"> </span><span class="kw">cast_dtm</span>(document, term, count)</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>
<p>We can also generate a Document-feature matrix by using the <code>cast_dfm</code> function</p>
<div class="sourceCode" id="cb179"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb179-1" title="1">ap_td <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb179-2" title="2"><span class="st"> </span><span class="kw">cast_dfm</span>(document, term, count)</a></code></pre></div>
<pre><code>## Document-feature matrix of: 2,246 documents, 10,473 features (98.7% sparse).
## features
## docs adding adult ago alcohol allegedly allen apparently appeared arrested
## 1 1 2 1 1 1 1 2 1 1
## 2 0 0 0 0 0 0 0 1 0
## 3 0 0 1 0 0 0 0 1 0
## 4 0 0 3 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 2 0 0 0 0 0 0
## features
## docs assault
## 1 1
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## [ reached max_ndoc ... 2,240 more documents, reached max_nfeat ... 10,463 more features ]</code></pre>
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