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<!DOCTYPE html>
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<title>Chapter 2 Text processing | 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>
<li class="chapter" data-level="2.2" data-path="text-processing.html"><a href="text-processing.html#nlp-applications"><i class="fa fa-check"></i><b>2.2</b> NLP applications</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="3.1" data-path="Word-embeddings.html"><a href="Word-embeddings.html#vectorizing-text"><i class="fa fa-check"></i><b>3.1</b> Vectorizing text</a></li>
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<li class="chapter" data-level="3.4.1" data-path="Word-embeddings.html"><a href="Word-embeddings.html#using-skip-gram"><i class="fa fa-check"></i><b>3.4.1</b> Using Skip-Gram</a></li>
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<li class="chapter" data-level="4.1" data-path="text-classification.html"><a href="text-classification.html#load-the-data"><i class="fa fa-check"></i><b>4.1</b> Load the data</a></li>
<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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<section class="normal" id="section-">
<div id="text-processing" class="section level1">
<h1><span class="header-section-number">Chapter 2</span> Text processing</h1>
<div id="text-data" class="section level2">
<h2><span class="header-section-number">2.1</span> Text data</h2>
<ul>
<li>Text data can be understood as sequences of characters or sequences of words</li>
</ul>
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<div id="nlp-applications" class="section level2">
<h2><span class="header-section-number">2.2</span> NLP applications</h2>
<ul>
<li>Document classification</li>
<li>Sentiment analysis</li>
<li>Author identification</li>
<li>Question answering</li>
<li>Topic modeling</li>
</ul>
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<div id="tokenization" class="section level2">
<h2><span class="header-section-number">2.3</span> Tokenization</h2>
<p>It consists of defining the unit of analysis. This might include words, sequences of words, or entire sentences. We can tokenize text at verious units including: charcters, words, sentenses, lines, paragraphs, and n-grams.</p>
<ul>
<li><p>N-grams: An n-gram is a term in linguistics for a continious sequence of n items from a given sequence of text or speech. The item can be phonemes, syllabes, letters, or words depending on the application, but when most people talk about n-grames, they mean a group of n words. Examples: unigrams (“hello”, “day”, “work”), bigrams (“good day”, “hello world”), trigrams (“tou and me”, “day of work”).</p></li>
<li><p>Bag of words: When we extract n-grams from a text documents, the collection of these n-grams are called <em>bag of words</em>, since the tokens have no specific order.</p></li>
</ul>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="co"># load text data</span></a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw">library</span>(janeaustenr)</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">library</span>(dplyr)</a></code></pre></div>
<pre><code>##
## Attaching package: 'dplyr'</code></pre>
<pre><code>## The following objects are masked from 'package:stats':
##
## filter, lag</code></pre>
<pre><code>## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union</code></pre>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw">library</span>(stringr)</a>
<a class="sourceLine" id="cb5-2" title="2"></a>
<a class="sourceLine" id="cb5-3" title="3">original_books <-<span class="st"> </span><span class="kw">austen_books</span>() <span class="op">%>%</span></a>
<a class="sourceLine" id="cb5-4" title="4"><span class="st"> </span><span class="kw">group_by</span>(book) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb5-5" title="5"><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="cb5-6" title="6"> <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="cb5-7" title="7"> <span class="dt">ignore_case =</span> <span class="ot">TRUE</span>)))) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb5-8" title="8"><span class="st"> </span><span class="kw">ungroup</span>()</a>
<a class="sourceLine" id="cb5-9" title="9"></a>
<a class="sourceLine" id="cb5-10" title="10">original_books</a></code></pre></div>
<pre><code>## # A tibble: 73,422 x 4
## text book linenumber chapter
## <chr> <fct> <int> <int>
## 1 "SENSE AND SENSIBILITY" Sense & Sensibility 1 0
## 2 "" Sense & Sensibility 2 0
## 3 "by Jane Austen" Sense & Sensibility 3 0
## 4 "" Sense & Sensibility 4 0
## 5 "(1811)" Sense & Sensibility 5 0
## 6 "" Sense & Sensibility 6 0
## 7 "" Sense & Sensibility 7 0
## 8 "" Sense & Sensibility 8 0
## 9 "" Sense & Sensibility 9 0
## 10 "CHAPTER 1" Sense & Sensibility 10 1
## # ... with 73,412 more rows</code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="co"># tokenization</span></a>
<a class="sourceLine" id="cb7-2" title="2"><span class="kw">library</span>(tidytext)</a>
<a class="sourceLine" id="cb7-3" title="3">tidy_books <-<span class="st"> </span>original_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb7-4" title="4"><span class="st"> </span><span class="kw">unnest_tokens</span>(word, text)</a>
<a class="sourceLine" id="cb7-5" title="5"></a>
<a class="sourceLine" id="cb7-6" title="6">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>This function uses the <code>tokenizer</code> package to sperate each line of text into tokens. By default, it performs a word tokenization but we can select other options for chearcters, n-grams, sentences, lines, paragraphs…</p>
</div>
<div id="stop-words-handeling" class="section level2">
<h2><span class="header-section-number">2.4</span> Stop words handeling</h2>
<p>Often in text analysis, we will want to remove stop words; stop words are words that are not useful for an analysis, typically extremely common words such as “the”, “of”, “to”, and so forth in English.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="kw">data</span>(stop_words)</a>
<a class="sourceLine" id="cb9-2" title="2"></a>
<a class="sourceLine" id="cb9-3" title="3">tidy_books <-<span class="st"> </span>tidy_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb9-4" title="4"><span class="st"> </span><span class="kw">anti_join</span>(stop_words)</a></code></pre></div>
<pre><code>## Joining, by = "word"</code></pre>
</div>
<div id="words-frequencies" class="section level2">
<h2><span class="header-section-number">2.5</span> Words frequencies</h2>
<ul>
<li>Find the most common words in all the books</li>
</ul>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1">tidy_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb11-2" title="2"><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>## # A tibble: 13,914 x 2
## word n
## <chr> <int>
## 1 miss 1855
## 2 time 1337
## 3 fanny 862
## 4 dear 822
## 5 lady 817
## 6 sir 806
## 7 day 797
## 8 emma 787
## 9 sister 727
## 10 house 699
## # ... with 13,904 more rows</code></pre>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1"><span class="co"># plot the most common words</span></a>
<a class="sourceLine" id="cb13-2" title="2"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb13-3" title="3"></a>
<a class="sourceLine" id="cb13-4" title="4">tidy_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb13-5" title="5"><span class="st"> </span><span class="kw">count</span>(word, <span class="dt">sort =</span> <span class="ot">TRUE</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb13-6" title="6"><span class="st"> </span><span class="kw">filter</span>(n <span class="op">></span><span class="st"> </span><span class="dv">600</span>) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb13-7" title="7"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">word =</span> <span class="kw">reorder</span>(word, n)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb13-8" title="8"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(word, n)) <span class="op">+</span></a>
<a class="sourceLine" id="cb13-9" title="9"><span class="st"> </span><span class="kw">geom_col</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb13-10" title="10"><span class="st"> </span><span class="kw">xlab</span>(<span class="ot">NULL</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb13-11" title="11"><span class="st"> </span><span class="kw">coord_flip</span>()</a></code></pre></div>
<p><img src="NLP-book_files/figure-html/unnamed-chunk-3-1.png" width="672" /></p>
<ul>
<li>plotting a wordclouds</li>
</ul>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1"><span class="kw">library</span>(wordcloud)</a></code></pre></div>
<pre><code>## Loading required package: RColorBrewer</code></pre>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" title="1">tidy_books <span class="op">%>%</span></a>
<a class="sourceLine" id="cb16-2" title="2"><span class="st"> </span><span class="kw">anti_join</span>(stop_words) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb16-3" title="3"><span class="st"> </span><span class="kw">count</span>(word) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb16-4" title="4"><span class="st"> </span><span class="kw">with</span>(<span class="kw">wordcloud</span>(word, n, <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-4-1.png" width="672" /></p>
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