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<title>Chapter 9 Words’ relationships analysis | Natural Language Processing with R</title>
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<div id="words-relationships-analysis" class="section level1">
<h1><span class="header-section-number">Chapter 9</span> Words’ relationships analysis</h1>
<p>Some intereseting text analysis techniques consists of quantifying the relationships betwwen words. These analysis help at examining for example which words tend to follow others or to occur within the same documents.</p>
<div id="extracting-bi-grams" class="section level2">
<h2><span class="header-section-number">9.1</span> Extracting bi-grams</h2>
<p>In order to analyze pairs of words, we can extract the different bi-grams from a corpus of text.</p>
<div class="sourceCode" id="cb152"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb152-1" title="1"><span class="kw">library</span>(dplyr)</a>
<a class="sourceLine" id="cb152-2" title="2"><span class="kw">library</span>(tidytext)</a>
<a class="sourceLine" id="cb152-3" title="3"><span class="kw">library</span>(janeaustenr)</a>
<a class="sourceLine" id="cb152-4" title="4"><span class="co"># extracting bi-grams</span></a>
<a class="sourceLine" id="cb152-5" title="5">austen_bigrams =<span class="st"> </span><span class="kw">austen_books</span>() <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb152-6" title="6"><span class="st"> </span><span class="kw">unnest_tokens</span>(bigram, text, <span class="dt">token =</span> <span class="st">"ngrams"</span>, <span class="dt">n =</span> <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb152-7" title="7"></a>
<a class="sourceLine" id="cb152-8" title="8">austen_bigrams</a></code></pre></div>
<pre><code>## # A tibble: 725,049 x 2
## book bigram
## <fct> <chr>
## 1 Sense & Sensibility sense and
## 2 Sense & Sensibility and sensibility
## 3 Sense & Sensibility sensibility by
## 4 Sense & Sensibility by jane
## 5 Sense & Sensibility jane austen
## 6 Sense & Sensibility austen 1811
## 7 Sense & Sensibility 1811 chapter
## 8 Sense & Sensibility chapter 1
## 9 Sense & Sensibility 1 the
## 10 Sense & Sensibility the family
## # ... with 725,039 more rows</code></pre>
<div class="sourceCode" id="cb154"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb154-1" title="1"><span class="co"># counting and filtering bi-grams</span></a>
<a class="sourceLine" id="cb154-2" title="2"></a>
<a class="sourceLine" id="cb154-3" title="3">austen_bigrams <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb154-4" title="4"><span class="st"> </span><span class="kw">count</span>(bigram, <span class="dt">sort =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<pre><code>## # A tibble: 211,236 x 2
## bigram n
## <chr> <int>
## 1 of the 3017
## 2 to be 2787
## 3 in the 2368
## 4 it was 1781
## 5 i am 1545
## 6 she had 1472
## 7 of her 1445
## 8 to the 1387
## 9 she was 1377
## 10 had been 1299
## # ... with 211,226 more rows</code></pre>
<p>Since the resulting dataframe contains some stop words, we can attempt to remove them by seperating the bigrams,
filtering the stop words and recombinng them after:</p>
<div class="sourceCode" id="cb156"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb156-1" title="1"><span class="co"># seperating bigrams by splitting the "bugram" column </span></a>
<a class="sourceLine" id="cb156-2" title="2"><span class="kw">library</span>(tidyr)</a>
<a class="sourceLine" id="cb156-3" title="3"></a>
<a class="sourceLine" id="cb156-4" title="4">bigrams_separated <-<span class="st"> </span>austen_bigrams <span class="op">%>%</span></a>
<a class="sourceLine" id="cb156-5" title="5"><span class="st"> </span><span class="kw">separate</span>(bigram, <span class="kw">c</span>(<span class="st">"word1"</span>, <span class="st">"word2"</span>), <span class="dt">sep =</span> <span class="st">" "</span>)</a>
<a class="sourceLine" id="cb156-6" title="6"></a>
<a class="sourceLine" id="cb156-7" title="7"><span class="co"># removing stop words</span></a>
<a class="sourceLine" id="cb156-8" title="8">bigrams_filtered <-<span class="st"> </span>bigrams_separated <span class="op">%>%</span></a>
<a class="sourceLine" id="cb156-9" title="9"><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span>word1 <span class="op">%in%</span><span class="st"> </span>stop_words<span class="op">$</span>word) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb156-10" title="10"><span class="st"> </span><span class="kw">filter</span>(<span class="op">!</span>word2 <span class="op">%in%</span><span class="st"> </span>stop_words<span class="op">$</span>word)</a>
<a class="sourceLine" id="cb156-11" title="11"></a>
<a class="sourceLine" id="cb156-12" title="12"><span class="co"># new bigrams count</span></a>
<a class="sourceLine" id="cb156-13" title="13">bigram_counts =<span class="st"> </span>bigrams_filtered <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb156-14" title="14"><span class="st"> </span><span class="kw">count</span>(word1, word2, <span class="dt">sort =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb156-15" title="15"></a>
<a class="sourceLine" id="cb156-16" title="16">bigram_counts</a></code></pre></div>
<pre><code>## # A tibble: 33,421 x 3
## word1 word2 n
## <chr> <chr> <int>
## 1 sir thomas 287
## 2 miss crawford 215
## 3 captain wentworth 170
## 4 miss woodhouse 162
## 5 frank churchill 132
## 6 lady russell 118
## 7 lady bertram 114
## 8 sir walter 113
## 9 miss fairfax 109
## 10 colonel brandon 108
## # ... with 33,411 more rows</code></pre>
<div class="sourceCode" id="cb158"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb158-1" title="1"><span class="co"># recombing bigrams</span></a>
<a class="sourceLine" id="cb158-2" title="2">bigrams_united <-<span class="st"> </span>bigrams_filtered <span class="op">%>%</span></a>
<a class="sourceLine" id="cb158-3" title="3"><span class="st"> </span><span class="kw">unite</span>(bigram, word1, word2, <span class="dt">sep =</span> <span class="st">" "</span>)</a>
<a class="sourceLine" id="cb158-4" title="4">bigrams_united</a></code></pre></div>
<pre><code>## # A tibble: 44,784 x 2
## book bigram
## <fct> <chr>
## 1 Sense & Sensibility jane austen
## 2 Sense & Sensibility austen 1811
## 3 Sense & Sensibility 1811 chapter
## 4 Sense & Sensibility chapter 1
## 5 Sense & Sensibility norland park
## 6 Sense & Sensibility surrounding acquaintance
## 7 Sense & Sensibility late owner
## 8 Sense & Sensibility advanced age
## 9 Sense & Sensibility constant companion
## 10 Sense & Sensibility happened ten
## # ... with 44,774 more rows</code></pre>
</div>
<div id="analyzing-bi-grams" class="section level2">
<h2><span class="header-section-number">9.2</span> Analyzing bi-grams</h2>
<p>Once we have the list of bi-grams filtered from stop words, we can perform some statistical anaysis by computing for example the TF-IDF values</p>
<div class="sourceCode" id="cb160"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb160-1" title="1"><span class="co"># Measuring the tf-idf values of bigrams</span></a>
<a class="sourceLine" id="cb160-2" title="2">bigram_tf_idf =<span class="st"> </span>bigrams_united <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb160-3" title="3"><span class="st"> </span><span class="kw">count</span>(book, bigram) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb160-4" title="4"><span class="st"> </span><span class="kw">bind_tf_idf</span>(bigram, book, n) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb160-5" title="5"><span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(tf_idf))</a>
<a class="sourceLine" id="cb160-6" title="6"></a>
<a class="sourceLine" id="cb160-7" title="7">bigram_tf_idf</a></code></pre></div>
<pre><code>## # A tibble: 36,217 x 6
## book bigram n tf idf tf_idf
## <fct> <chr> <int> <dbl> <dbl> <dbl>
## 1 Persuasion captain wentworth 170 0.0299 1.79 0.0535
## 2 Mansfield Park sir thomas 287 0.0287 1.79 0.0515
## 3 Mansfield Park miss crawford 215 0.0215 1.79 0.0386
## 4 Persuasion lady russell 118 0.0207 1.79 0.0371
## 5 Persuasion sir walter 113 0.0198 1.79 0.0356
## 6 Emma miss woodhouse 162 0.0170 1.79 0.0305
## 7 Northanger Abbey miss tilney 82 0.0159 1.79 0.0286
## 8 Sense & Sensibility colonel brandon 108 0.0150 1.79 0.0269
## 9 Emma frank churchill 132 0.0139 1.79 0.0248
## 10 Pride & Prejudice lady catherine 100 0.0138 1.79 0.0247
## # ... with 36,207 more rows</code></pre>
<div class="sourceCode" id="cb162"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb162-1" title="1"><span class="co"># plotting the results</span></a>
<a class="sourceLine" id="cb162-2" title="2">bigram_tf_idf <span class="op">%>%</span></a>
<a class="sourceLine" id="cb162-3" title="3"><span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(tf_idf)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb162-4" title="4"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">bigram =</span> <span class="kw">factor</span>(bigram, <span class="dt">levels =</span> <span class="kw">rev</span>(<span class="kw">unique</span>(bigram)))) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb162-5" title="5"><span class="st"> </span><span class="kw">group_by</span>(book) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb162-6" title="6"><span class="st"> </span><span class="kw">top_n</span>(<span class="dv">15</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb162-7" title="7"><span class="st"> </span><span class="kw">ungroup</span>() <span class="op">%>%</span></a>
<a class="sourceLine" id="cb162-8" title="8"><span class="st"> </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(bigram, tf_idf, <span class="dt">fill =</span> book)) <span class="op">+</span></a>
<a class="sourceLine" id="cb162-9" title="9"><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="cb162-10" title="10"><span class="st"> </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="ot">NULL</span>, <span class="dt">y =</span> <span class="st">"tf-idf"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb162-11" title="11"><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"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb162-12" title="12"><span class="st"> </span><span class="kw">coord_flip</span>()</a></code></pre></div>
<p><img src="NLP-book_files/figure-html/unnamed-chunk-24-1.png" width="672" /></p>
</div>
<div id="visualizing-a-network-of-bigrams" class="section level2">
<h2><span class="header-section-number">9.3</span> Visualizing a network of bigrams</h2>
<p>The relationships between words can be visualized as a graph where nodes represent the words and edges represent the bigram connections. In order to make graph visualizing, we will start by transforming our dataframe <code>bigram_counts</code> into a graph.</p>
<div class="sourceCode" id="cb163"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb163-1" title="1"><span class="kw">library</span>(igraph)</a>
<a class="sourceLine" id="cb163-2" title="2"><span class="co"># original dataframe</span></a>
<a class="sourceLine" id="cb163-3" title="3">bigram_counts</a></code></pre></div>
<pre><code>## # A tibble: 33,421 x 3
## word1 word2 n
## <chr> <chr> <int>
## 1 sir thomas 287
## 2 miss crawford 215
## 3 captain wentworth 170
## 4 miss woodhouse 162
## 5 frank churchill 132
## 6 lady russell 118
## 7 lady bertram 114
## 8 sir walter 113
## 9 miss fairfax 109
## 10 colonel brandon 108
## # ... with 33,411 more rows</code></pre>
<div class="sourceCode" id="cb165"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb165-1" title="1"><span class="co"># filter common combinations</span></a>
<a class="sourceLine" id="cb165-2" title="2">bigram_graph =<span class="st"> </span>bigram_counts <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb165-3" title="3"><span class="st"> </span><span class="kw">filter</span>(n <span class="op">></span><span class="st"> </span><span class="dv">20</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb165-4" title="4"><span class="st"> </span><span class="kw">graph_from_data_frame</span>()</a>
<a class="sourceLine" id="cb165-5" title="5">bigram_graph</a></code></pre></div>
<pre><code>## IGRAPH ab4e2d9 DN-- 91 77 --
## + attr: name (v/c), n (e/n)
## + edges from ab4e2d9 (vertex names):
## [1] sir ->thomas miss ->crawford captain ->wentworth
## [4] miss ->woodhouse frank ->churchill lady ->russell
## [7] lady ->bertram sir ->walter miss ->fairfax
## [10] colonel ->brandon miss ->bates lady ->catherine
## [13] sir ->john jane ->fairfax miss ->tilney
## [16] lady ->middleton miss ->bingley thousand->pounds
## [19] miss ->dashwood miss ->bennet john ->knightley
## [22] miss ->morland captain ->benwick dear ->miss
## + ... omitted several edges</code></pre>
<p>Now we can use the <code>ggraph</code> package in order to make a beautiful visulization of our words graph by specifying the direction of connections.</p>
<div class="sourceCode" id="cb167"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb167-1" title="1"><span class="kw">library</span>(ggraph)</a>
<a class="sourceLine" id="cb167-2" title="2"><span class="kw">set.seed</span>(<span class="dv">2016</span>)</a>
<a class="sourceLine" id="cb167-3" title="3"></a>
<a class="sourceLine" id="cb167-4" title="4">a <-<span class="st"> </span>grid<span class="op">::</span><span class="kw">arrow</span>(<span class="dt">type =</span> <span class="st">"closed"</span>, <span class="dt">length =</span> <span class="kw">unit</span>(.<span class="dv">15</span>, <span class="st">"inches"</span>))</a>
<a class="sourceLine" id="cb167-5" title="5"></a>
<a class="sourceLine" id="cb167-6" title="6"><span class="kw">ggraph</span>(bigram_graph, <span class="dt">layout =</span> <span class="st">"fr"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb167-7" title="7"><span class="st"> </span><span class="kw">geom_edge_link</span>(<span class="kw">aes</span>(<span class="dt">edge_alpha =</span> n), <span class="dt">show.legend =</span> <span class="ot">FALSE</span>,</a>
<a class="sourceLine" id="cb167-8" title="8"> <span class="dt">arrow =</span> a, <span class="dt">end_cap =</span> <span class="kw">circle</span>(.<span class="dv">07</span>, <span class="st">'inches'</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb167-9" title="9"><span class="st"> </span><span class="kw">geom_node_point</span>(<span class="dt">color =</span> <span class="st">"lightblue"</span>, <span class="dt">size =</span> <span class="dv">3</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb167-10" title="10"><span class="st"> </span><span class="kw">geom_node_text</span>(<span class="kw">aes</span>(<span class="dt">label =</span> name), <span class="dt">vjust =</span> <span class="dv">1</span>, <span class="dt">hjust =</span> <span class="dv">1</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb167-11" title="11"><span class="st"> </span><span class="kw">theme_void</span>()</a></code></pre></div>
<p><img src="NLP-book_files/figure-html/unnamed-chunk-26-1.png" width="672" /></p>
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