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<!DOCTYPE html>
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<title>Chapter 6 Sentiment Analysis | 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>
<li class="chapter" data-level="2.1" data-path="text-processing.html"><a href="text-processing.html#text-data"><i class="fa fa-check"></i><b>2.1</b> Text data</a></li>
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<li class="chapter" data-level="2.5" data-path="text-processing.html"><a href="text-processing.html#words-frequencies"><i class="fa fa-check"></i><b>2.5</b> Words frequencies</a></li>
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<li class="chapter" data-level="4.2" data-path="text-classification.html"><a href="text-classification.html#prepare-the-data-for-neural-network"><i class="fa fa-check"></i><b>4.2</b> Prepare the data for neural network</a></li>
<li class="chapter" data-level="4.3" data-path="text-classification.html"><a href="text-classification.html#building-the-model"><i class="fa fa-check"></i><b>4.3</b> Building the model</a></li>
<li class="chapter" data-level="4.4" data-path="text-classification.html"><a href="text-classification.html#testing-the-model"><i class="fa fa-check"></i><b>4.4</b> Testing the model</a></li>
<li class="chapter" data-level="4.5" data-path="text-classification.html"><a href="text-classification.html#reference"><i class="fa fa-check"></i><b>4.5</b> Reference</a></li>
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<li class="chapter" data-level="5" data-path="RNN.html"><a href="RNN.html"><i class="fa fa-check"></i><b>5</b> Reccurent Neural Networks (RNN)</a><ul>
<li class="chapter" data-level="5.1" data-path="RNN.html"><a href="RNN.html#understanding-recurrent-neural-network"><i class="fa fa-check"></i><b>5.1</b> Understanding Recurrent Neural Network</a></li>
<li class="chapter" data-level="5.2" data-path="RNN.html"><a href="RNN.html#rnn-with-keras"><i class="fa fa-check"></i><b>5.2</b> RNN with Keras</a></li>
<li class="chapter" data-level="5.3" data-path="RNN.html"><a href="RNN.html#lstm-with-keras"><i class="fa fa-check"></i><b>5.3</b> LSTM with Keras</a></li>
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<li class="chapter" data-level="6" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html"><i class="fa fa-check"></i><b>6</b> Sentiment Analysis</a><ul>
<li class="chapter" data-level="6.1" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html#the-sentiments-dataset"><i class="fa fa-check"></i><b>6.1</b> The “Sentiments” dataset</a></li>
<li class="chapter" data-level="6.2" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html#application"><i class="fa fa-check"></i><b>6.2</b> Application</a></li>
<li class="chapter" data-level="6.3" data-path="sentiment-analysis.html"><a href="sentiment-analysis.html#references-1"><i class="fa fa-check"></i><b>6.3</b> References:</a></li>
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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>
<li class="chapter" data-level="7.2" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html#zipfs-law"><i class="fa fa-check"></i><b>7.2</b> Zipf’s law</a></li>
<li class="chapter" data-level="7.3" data-path="word-and-document-frequency-tf-idf.html"><a href="word-and-document-frequency-tf-idf.html#tf_idf-metric"><i class="fa fa-check"></i><b>7.3</b> TF_IDF metric</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="9" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html"><i class="fa fa-check"></i><b>9</b> Words’ relationships analysis</a><ul>
<li class="chapter" data-level="9.1" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html#extracting-bi-grams"><i class="fa fa-check"></i><b>9.1</b> Extracting bi-grams</a></li>
<li class="chapter" data-level="9.2" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html#analyzing-bi-grams"><i class="fa fa-check"></i><b>9.2</b> Analyzing bi-grams</a></li>
<li class="chapter" data-level="9.3" data-path="words-relationships-analysis.html"><a href="words-relationships-analysis.html#visualizing-a-network-of-bigrams"><i class="fa fa-check"></i><b>9.3</b> Visualizing a network of bigrams</a></li>
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<li class="chapter" data-level="10" data-path="document-term-matrix.html"><a href="document-term-matrix.html"><i class="fa fa-check"></i><b>10</b> Document-term matrix</a><ul>
<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>
<li class="chapter" data-level="10.2" data-path="document-term-matrix.html"><a href="document-term-matrix.html#generating-document-term-matrix"><i class="fa fa-check"></i><b>10.2</b> Generating Document-term matrix</a></li>
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<div id="sentiment-analysis" class="section level1">
<h1><span class="header-section-number">Chapter 6</span> Sentiment Analysis</h1>
<div id="the-sentiments-dataset" class="section level2">
<h2><span class="header-section-number">6.1</span> The “Sentiments” dataset</h2>
<p>There are several ethods and dictionaries that we can use for evaluating the opinion or emotion in text. We can site:</p>
<ul>
<li><code>AFINN</code>: <a href="http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010" class="uri">http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010</a></li>
<li><code>bing</code>: <a href="https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html" class="uri">https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html</a></li>
<li><code>nrc</code>: <a href="http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm" class="uri">http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm</a></li>
</ul>
<p>These datsets contain many English words with assigned scores for positive/negative sentiment and emotions (joy, anger, sadness…). These data are mainly constructed via crowdsourcing tools (for example: Amazon Mechanical Turk) and valiated based by using available dataset such as movie review, Twitter… They are only based on unigrams so they don’t take inot account negation (“no good”, “not sad”…).</p>
<div class="sourceCode" id="cb104"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb104-1" title="1"><span class="kw">library</span>(tidytext)</a>
<a class="sourceLine" id="cb104-2" title="2"><span class="kw">library</span>(textdata)</a></code></pre></div>
<pre><code>##
## Attaching package: 'textdata'</code></pre>
<pre><code>## The following object is masked from 'package:keras':
##
## dataset_imdb</code></pre>
<div class="sourceCode" id="cb107"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb107-1" title="1"><span class="kw">get_sentiments</span>(<span class="st">"bing"</span>)</a></code></pre></div>
<pre><code>## # A tibble: 6,786 x 2
## word sentiment
## <chr> <chr>
## 1 2-faces negative
## 2 abnormal negative
## 3 abolish negative
## 4 abominable negative
## 5 abominably negative
## 6 abominate negative
## 7 abomination negative
## 8 abort negative
## 9 aborted negative
## 10 aborts negative
## # ... with 6,776 more rows</code></pre>
<div class="sourceCode" id="cb109"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb109-1" title="1"><span class="kw">get_sentiments</span>(<span class="st">"nrc"</span>)</a></code></pre></div>
<pre><code>## # A tibble: 13,901 x 2
## word sentiment
## <chr> <chr>
## 1 abacus trust
## 2 abandon fear
## 3 abandon negative
## 4 abandon sadness
## 5 abandoned anger
## 6 abandoned fear
## 7 abandoned negative
## 8 abandoned sadness
## 9 abandonment anger
## 10 abandonment fear
## # ... with 13,891 more rows</code></pre>
</div>
<div id="application" class="section level2">
<h2><span class="header-section-number">6.2</span> Application</h2>
<p>We can implement sentiment analysis by joinin text data with setiment dataset. Here is an example for finding the most common “joy” words in “Emma” book from “jane austen” autor ny using the “nrc” lexicon.</p>
<p>We prepare the text data by getting words tokens</p>
<div class="sourceCode" id="cb111"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb111-1" title="1">tidy_books <-<span class="st"> </span><span class="kw">austen_books</span>() <span class="op">%>%</span></a>
<a class="sourceLine" id="cb111-2" title="2"><span class="st"> </span><span class="kw">group_by</span>(book) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb111-3" title="3"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">linenumber =</span> <span class="kw">row_number</span>(),</a>
<a class="sourceLine" id="cb111-4" title="4"> <span class="dt">chapter =</span> <span class="kw">cumsum</span>(<span class="kw">str_detect</span>(text, <span class="kw">regex</span>(<span class="st">"^chapter [</span><span class="ch">\\</span><span class="st">divxlc]"</span>, </a>
<a class="sourceLine" id="cb111-5" title="5"> <span class="dt">ignore_case =</span> <span class="ot">TRUE</span>)))) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb111-6" title="6"><span class="st"> </span><span class="kw">ungroup</span>() <span class="op">%>%</span></a>
<a class="sourceLine" id="cb111-7" title="7"><span class="st"> </span><span class="kw">unnest_tokens</span>(word, text)</a>
<a class="sourceLine" id="cb111-8" title="8">tidy_books</a></code></pre></div>
<pre><code>## # A tibble: 725,055 x 4
## book linenumber chapter word
## <fct> <int> <int> <chr>
## 1 Sense & Sensibility 1 0 sense
## 2 Sense & Sensibility 1 0 and
## 3 Sense & Sensibility 1 0 sensibility
## 4 Sense & Sensibility 3 0 by
## 5 Sense & Sensibility 3 0 jane
## 6 Sense & Sensibility 3 0 austen
## 7 Sense & Sensibility 5 0 1811
## 8 Sense & Sensibility 10 1 chapter
## 9 Sense & Sensibility 10 1 1
## 10 Sense & Sensibility 13 1 the
## # ... with 725,045 more rows</code></pre>
<p>Now we have text in a tidy format with one word per row. We filter the “joy” words from NRC lexicon and join them with the words in “Emma” book.</p>
<div class="sourceCode" id="cb113"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb113-1" title="1">nrc_joy <-<span class="st"> </span><span class="kw">get_sentiments</span>(<span class="st">"nrc"</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb113-2" title="2"><span class="st"> </span><span class="kw">filter</span>(sentiment <span class="op">==</span><span class="st"> "joy"</span>)</a>
<a class="sourceLine" id="cb113-3" title="3"></a>
<a class="sourceLine" id="cb113-4" title="4">tidy_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb113-5" title="5"><span class="st"> </span><span class="kw">filter</span>(book <span class="op">==</span><span class="st"> "Emma"</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb113-6" title="6"><span class="st"> </span><span class="kw">inner_join</span>(nrc_joy) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb113-7" title="7"><span class="st"> </span><span class="kw">count</span>(word, <span class="dt">sort =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<pre><code>## Joining, by = "word"</code></pre>
<pre><code>## # A tibble: 303 x 2
## word n
## <chr> <int>
## 1 good 359
## 2 young 192
## 3 friend 166
## 4 hope 143
## 5 happy 125
## 6 love 117
## 7 deal 92
## 8 found 92
## 9 present 89
## 10 kind 82
## # ... with 293 more rows</code></pre>
<p>Here is another example of using the “bing” lexicon to count the number of positive versus negative words in the different books.</p>
<div class="sourceCode" id="cb116"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb116-1" title="1"><span class="co"># prepare the data b calculating sentiment score</span></a>
<a class="sourceLine" id="cb116-2" title="2"><span class="kw">library</span>(tidyr)</a>
<a class="sourceLine" id="cb116-3" title="3"></a>
<a class="sourceLine" id="cb116-4" title="4">jane_austen_sentiment <-<span class="st"> </span>tidy_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb116-5" title="5"><span class="st"> </span><span class="kw">inner_join</span>(<span class="kw">get_sentiments</span>(<span class="st">"bing"</span>)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb116-6" title="6"><span class="st"> </span><span class="kw">count</span>(book, <span class="dt">index =</span> linenumber <span class="op">%/%</span><span class="st"> </span><span class="dv">80</span>, sentiment) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb116-7" title="7"><span class="st"> </span><span class="kw">spread</span>(sentiment, n, <span class="dt">fill =</span> <span class="dv">0</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb116-8" title="8"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">sentiment =</span> positive <span class="op">-</span><span class="st"> </span>negative)</a></code></pre></div>
<pre><code>## Joining, by = "word"</code></pre>
<div class="sourceCode" id="cb118"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb118-1" title="1">jane_austen_sentiment</a></code></pre></div>
<pre><code>## # A tibble: 920 x 5
## book index negative positive sentiment
## <fct> <dbl> <dbl> <dbl> <dbl>
## 1 Sense & Sensibility 0 16 32 16
## 2 Sense & Sensibility 1 19 53 34
## 3 Sense & Sensibility 2 12 31 19
## 4 Sense & Sensibility 3 15 31 16
## 5 Sense & Sensibility 4 16 34 18
## 6 Sense & Sensibility 5 16 51 35
## 7 Sense & Sensibility 6 24 40 16
## 8 Sense & Sensibility 7 23 51 28
## 9 Sense & Sensibility 8 30 40 10
## 10 Sense & Sensibility 9 15 19 4
## # ... with 910 more rows</code></pre>
<div class="sourceCode" id="cb120"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb120-1" title="1"><span class="co"># plot the result by book</span></a>
<a class="sourceLine" id="cb120-2" title="2"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb120-3" title="3"></a>
<a class="sourceLine" id="cb120-4" title="4"><span class="kw">ggplot</span>(jane_austen_sentiment, <span class="kw">aes</span>(index, sentiment, <span class="dt">fill =</span> book)) <span class="op">+</span></a>
<a class="sourceLine" id="cb120-5" title="5"><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="cb120-6" title="6"><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span>book, <span class="dt">ncol =</span> <span class="dv">2</span>, <span class="dt">scales =</span> <span class="st">"free_x"</span>)</a></code></pre></div>
<p><img src="NLP-book_files/figure-html/unnamed-chunk-9-1.png" width="672" /></p>
<p>We can tag positive and negative words by using wordclouds</p>
<div class="sourceCode" id="cb121"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb121-1" title="1"><span class="kw">library</span>(reshape2)</a></code></pre></div>
<pre><code>##
## Attaching package: 'reshape2'</code></pre>
<pre><code>## The following object is masked from 'package:tidyr':
##
## smiths</code></pre>
<div class="sourceCode" id="cb124"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb124-1" title="1">tidy_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb124-2" title="2"><span class="st"> </span><span class="kw">inner_join</span>(<span class="kw">get_sentiments</span>(<span class="st">"bing"</span>)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb124-3" title="3"><span class="st"> </span><span class="kw">count</span>(word, sentiment, <span class="dt">sort =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb124-4" title="4"><span class="st"> </span><span class="kw">acast</span>(word <span class="op">~</span><span class="st"> </span>sentiment, <span class="dt">value.var =</span> <span class="st">"n"</span>, <span class="dt">fill =</span> <span class="dv">0</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb124-5" title="5"><span class="st"> </span><span class="kw">comparison.cloud</span>(<span class="dt">colors =</span> <span class="kw">c</span>(<span class="st">"red"</span>, <span class="st">"green"</span>),</a>
<a class="sourceLine" id="cb124-6" title="6"> <span class="dt">max.words =</span> <span class="dv">100</span>)</a></code></pre></div>
<pre><code>## Joining, by = "word"</code></pre>
<p><img src="NLP-book_files/figure-html/unnamed-chunk-10-1.png" width="672" /></p>
</div>
<div id="references-1" class="section level2">
<h2><span class="header-section-number">6.3</span> References:</h2>
<ul>
<li><a href="https://www.tidytextmining.com/sentiment.html" class="uri">https://www.tidytextmining.com/sentiment.html</a></li>
</ul>
</div>
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</section>
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