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describe the semantic relationships between concepts. In this network the concepts are significant words and
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    <h1>Create twitter semantic network</h1>
    <small class="dont-index">Source: <a href="https://github.com/vosonlab/vosonSML/blob/HEAD/R/Create.semantic.twitter.R" class="external-link"><code>R/Create.semantic.twitter.R</code></a></small>
    <div class="hidden name"><code>Create.semantic.twitter.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Creates a semantic network from tweets returned from the twitter search query. Semantic networks
describe the semantic relationships between concepts. In this network the concepts are significant words and
hashtags extracted from the tweet text. Network edges are weighted and represent occurrence of words and hashtags
in the same tweets.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="co"># S3 method for semantic.twitter</span></span>
<span><span class="fu"><a href="Create.html">Create</a></span><span class="op">(</span></span>
<span>  <span class="va">datasource</span>,</span>
<span>  <span class="va">type</span>,</span>
<span>  removeRetweets <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  removeTermsOrHashtags <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span>  stopwords <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  stopwordsLang <span class="op">=</span> <span class="st">"en"</span>,</span>
<span>  stopwordsSrc <span class="op">=</span> <span class="st">"smart"</span>,</span>
<span>  removeNumbers <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  removeUrls <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  termFreq <span class="op">=</span> <span class="fl">5</span>,</span>
<span>  hashtagFreq <span class="op">=</span> <span class="fl">50</span>,</span>
<span>  assoc <span class="op">=</span> <span class="st">"limited"</span>,</span>
<span>  verbose <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  <span class="va">...</span></span>
<span><span class="op">)</span></span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>
    <dl><dt>datasource</dt>
<dd><p>Collected social media data with <code>"datasource"</code> and <code>"twitter"</code> class names.</p></dd>


<dt>type</dt>
<dd><p>Character string. Type of network to be created, set to <code>"semantic"</code>.</p></dd>


<dt>removeRetweets</dt>
<dd><p>Logical. Removes detected retweets from the tweet data. Default is <code>TRUE</code>.</p></dd>


<dt>removeTermsOrHashtags</dt>
<dd><p>Character vector. Words or hashtags to remove from the semantic network. For example,
this parameter could be used to remove the search term or hashtag that was used to collect the data by removing any
nodes with matching name. Default is <code>NULL</code> to remove none.</p></dd>


<dt>stopwords</dt>
<dd><p>Logical. Removes stopwords from the tweet data. Default is <code>TRUE</code>.</p></dd>


<dt>stopwordsLang</dt>
<dd><p>Character string. Language of stopwords to use. Refer to the <span class="pkg">stopwords</span> package for further
information on supported languages. Default is <code>"en"</code>.</p></dd>


<dt>stopwordsSrc</dt>
<dd><p>Character string. Source of stopwords list. Refer to the <span class="pkg">stopwords</span> package for further
information on supported sources. Default is <code>"smart"</code>.</p></dd>


<dt>removeNumbers</dt>
<dd><p>Logical. Removes whole numerical tokens from the tweet text. For example, a year value such as
<code>2020</code> will be removed but not mixed values such as <code>G20</code>. Default is <code>TRUE</code>.</p></dd>


<dt>removeUrls</dt>
<dd><p>Logical. Removes twitter shortened URL tokens from the tweet text. Default is <code>TRUE</code>.</p></dd>


<dt>termFreq</dt>
<dd><p>Numeric integer. Specifies the percentage of most frequent words to include. For example,
<code>termFreq = 20</code> means that the 20 percent most frequently occurring <code>words</code> will be included in the
semantic network as nodes. A larger percentage will increase the number of nodes and therefore the size of graph.
The default value is <code>5</code>, meaning the top 5 percent most frequent words are used.</p></dd>


<dt>hashtagFreq</dt>
<dd><p>Numeric integer. Specifies the percentage of most frequent <code>hashtags</code> to include. For
example, <code>hashtagFreq = 20</code> means that the 20 percent most frequently occurring hashtags will be included in
the semantic network as nodes. The default value is <code>50</code>.</p></dd>


<dt>assoc</dt>
<dd><p>Character string. Association of nodes. A value of <code>"limited"</code> includes only edges between most
frequently occurring hashtags and terms. A value of <code>"full"</code> includes ties between most frequently occurring
hashtags and terms, hashtags and hashtags, and terms and terms. Default is <code>"limited"</code>.</p></dd>


<dt>verbose</dt>
<dd><p>Logical. Output additional information about the network creation. Default is <code>TRUE</code>.</p></dd>


<dt>...</dt>
<dd><p>Additional parameters passed to function. Not used in this method.</p></dd>

</dl></div>
    <div id="value">
    <h2>Value</h2>
    

<p>Network as a named list of two dataframes containing <code>$nodes</code> and <code>$edges</code>.</p>
    </div>
    <div id="note">
    <h2>Note</h2>
    <p>The words and hashtags passed to the function in the <code>removeTermsOrHashtags</code> parameter are removed before
word frequencies are calculated and are therefore excluded from top percentage of most frequent terms completely
rather than simply filtered out of the final network.</p>
<p>The top percentage of frequently occurring hashtags <code>hashtagFreq</code> and words <code>termFreq</code> are calculated to
a minimum frequency and all terms that have an equal or greater frequency than the minimum are included in the
network as nodes. For example, of unique hashtags of varying frequencies in a dataset the top 50% of total
frequency or most common hashtags may calculate to being the first 20 hashtags. The frequency of the 20th hashtag
is then used as the minimum and all hashtags of equal or greater frequency are included as part of the top 50% most
frequently occurring hashtags. So the number of top hashtags may end up being greater than 20 if there is more than
one hashtag that has frequency matching the minimum. The exception to this is if the minimum frequency is 1 and the
<code>hashtagFreq</code> is set to less than 100, in this case only the first 20 hashtags will be included.</p>
<p>Hashtags and words in the top percentages are included in the network as isolates if there are no instances of them
occurring in tweet text with other top percentage frequency terms.</p>
    </div>

    <div id="ref-examples">
    <h2>Examples</h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span><span class="co"># twitter semantic network creation additionally requires the stopwords</span></span></span>
<span class="r-in"><span><span class="co"># package for working with text data</span></span></span>
<span class="r-in"><span><span class="co"># install.packages("stopwords")</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># create a twitter semantic network graph removing the hashtag "#auspol"</span></span></span>
<span class="r-in"><span><span class="co"># and using the top 2% frequently occurring words and 10% most frequently</span></span></span>
<span class="r-in"><span><span class="co"># occurring hashtags as nodes</span></span></span>
<span class="r-in"><span><span class="va">net_semantic</span> <span class="op">&lt;-</span> <span class="va">collect_tw</span> <span class="op">|&gt;</span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="Create.html">Create</a></span><span class="op">(</span><span class="st">"semantic"</span>, removeTermsOrHashtags <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"#auspol"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span>         termFreq <span class="op">=</span> <span class="fl">2</span>, hashtagFreq <span class="op">=</span> <span class="fl">10</span>, verbose <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># network</span></span></span>
<span class="r-in"><span><span class="co"># net_semantic$nodes</span></span></span>
<span class="r-in"><span><span class="co"># net_semantic$edges</span></span></span>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-in"><span></span></span>
</code></pre></div>
    </div>
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