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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="X-UA-Compatible" content="IE=Edge" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>DARIAH Topics Explorer</title>
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Bokeh.set_log_level("info");
</script>
<style>
html {
width: 100%;
height: 100%;
}
body {
width: 90%;
height: 100%;
margin: auto;
}
</style>
</head>
<body>
<div class="document">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="section" id="dariah-topics-explorer">
<h1>DARIAH Topics Explorer<a class="headerlink" href="#dariah-topics-explorer" title="Permalink to this headline">¶</a></h1>
<div class="toctree-wrapper compound">
</div>
<p>This application presents a <strong>user-friendly topic modeling workflow</strong> consisting of data preprocessing, the actual modeling with <a class="reference external" href="http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf">latent Dirichlet allocation</a>, and the visualization of the model output to explore the semantic content of your text collection.</p>
<div class="admonition note">
<p class="last">If you do not know anything about topic modeling or programming in general, this is where you start.</p><br>
</div>
<div class="section" id="getting-started">
<h2>Getting started<a class="headerlink" href="#getting-started" title="Permalink to this headline">¶</a></h2>
<p>Windows and macOS users <strong>do not</strong> have to install additional software. The application itself is <a class="reference external" href="https://en.wikipedia.org/wiki/Portable_application">portable</a>.</p>
<ol class="arabic simple">
<li>Go to the <a class="reference external" href="https://github.com/DARIAH-DE/TopicsExplorer/releases/latest">release-section</a> and download the archive for your OS.</li>
<li>Extract the content of the archive.</li>
<li>Run the app by double-clicking the file <strong>DARIAH Topics Explorer</strong>.</li>
</ol>
<p>Linux users <strong>have to</strong> use the source code and install some dependencies, but Windows and macOS users can of course also do this:</p>
<ol class="arabic simple">
<li>Go to the <a class="reference external" href="https://github.com/DARIAH-DE/TopicsExplorer/releases/latest">release-section</a> and download the <strong>source code</strong> as ZIP archive.</li>
<li>Unzip the archive, e.g. using <code>unzip</code> via the command-line.</li>
<li>Make sure you have <a class="reference external" href="https://docs.pipenv.org/">Pipenv</a> installed (if not: use <code>pip3 install --user pipenv</code>).</li>
<li>Run <code>pipenv install</code> within the extracted archive, and afterwards <code>pipenv shell</code>.</li>
<li>To start the application, type <code>python topicsexplorer.py</code>, and press enter.</li>
</ol>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">If you want to use the sample corpus, you must clone the repository with Git (<strong>not</strong> download the source code from <strong>the release-section</strong>). See also section <a class="reference external" href="https://github.com/DARIAH-DE/TopicsExplorer#the-sample-corpus">The sample corpus</a> on GitHub.</p>
</div>
</div>
<div class="section" id="the-application">
<h2>The application<a class="headerlink" href="#about-this-application" title="Permalink to this headline">¶</a></h2>
<img alt="_static/screenshot.png" src="_static/screenshot.png" />
<p>Topics Explorer aims for <strong>simplicity and usability</strong>. If you are working with a large corpus (let’s say more than 200 documents, 5000 words each document), you may wish to use more sophisticated topic models such as those implemented
in <a class="reference external" href="http://mallet.cs.umass.edu/topics.php">MALLET</a>, which is known to be more robust than standard LDA. Have a look at our Jupyter notebook introducing <a class="reference external" href="https://github.com/DARIAH-DE/Topics/blob/master/IntroducingMallet.ipynb">topic modeling with MALLET</a>.</p>
</div>
<div class="section" id="example-visualization">
<h2>Example visualization<a class="headerlink" href="#example-visualization" title="Permalink to this headline">¶</a></h2>
<p>The following visualization is based on the distribution of 10 topics over a total of 10 novels (written by Charles Dickens, George Eliot, Joseph Fielding, William Thackeray, and Anthony Trollope). But first of all, the algorithm produces so-called topics:</p>
<p>
<center>
<table border="0" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Key 1</th>
<th>Key 2</th>
<th>Key 3</th>
<th>Key 4</th>
<th>Key 5</th>
</tr>
</thead>
<tbody>
<tr>
<th>Topic 1</th>
<td>captain</td>
<td>lord</td>
<td>whom</td>
<td>over</td>
<td>young</td>
</tr>
<tr>
<th>Topic 2</th>
<td>phineas</td>
<td>laura</td>
<td>lord</td>
<td>finn</td>
<td>kennedy</td>
</tr>
<tr>
<th>Topic 3</th>
<td>jarndyce</td>
<td>quite</td>
<td>sir</td>
<td>richard</td>
<td>ada</td>
</tr>
<tr>
<th>Topic 4</th>
<td>jones</td>
<td>indeed</td>
<td>adams</td>
<td>answered</td>
<td>may</td>
</tr>
<tr>
<th>Topic 5</th>
<td>our</td>
<td>these</td>
<td>can</td>
<td>honour</td>
<td>without</td>
</tr>
<tr>
<th>Topic 6</th>
<td>lopez</td>
<td>duke</td>
<td>wharton</td>
<td>course</td>
<td>duchess</td>
</tr>
<tr>
<th>Topic 7</th>
<td>crawley</td>
<td>george</td>
<td>osborne</td>
<td>rebecca</td>
<td>amelia</td>
</tr>
<tr>
<th>Topic 8</th>
<td>peggotty</td>
<td>aunt</td>
<td>mother</td>
<td>steerforth</td>
<td>murdstone</td>
</tr>
<tr>
<th>Topic 9</th>
<td>thought</td>
<td>way</td>
<td>too</td>
<td>down</td>
<td>went</td>
</tr>
<tr>
<th>Topic 10</th>
<td>tom</td>
<td>adam</td>
<td>maggie</td>
<td>work</td>
<td>tulliver</td>
</tr>
</tbody>
</table>
</center>
</p>
<p>These topics describe the semantic structures of a text corpus. Every document of the corpus consists, to a certain degree, of every topic. This distribution is visualized in a heatmap; the darker the blue, the higher the proportion.</p>
<p>
<div class="bk-root">
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