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<p>
<h1>Explore your own text collection with a topic model – without prior knowledge.</h1>
<p>The text mining technique <i>topic modeling</i> has become a popular procedure for clustering
documents
into semantic groups. This application introduces a user-friendly workflow which leads from raw
text
data to an interactive visualization of the topic model. All you need is a text corpus and a
little
time.</p>
<blockquote>Topic modeling algorithms are statistical methods that analyze the words of the
original texts
to discover the themes that run through them, how those themes are connected to each other, and
how
they change over time.
<footer>
<cite>
<a href="http://www.cs.columbia.edu/~blei/papers/Blei2012.pdf">David M. Blei</a>
</cite>
</footer>
</blockquote>
</p>
<h2>Overview</h2>
<ul>
<li>
<a href="#getting started">Getting started</a>
</li>
<li>
<a href="#the application">The application</a>
</li>
<li>
<a href="#the sample corpus">The sample corpus</a>
</li>
<li>
<a href="#example visualizations">Example visualizations</a>
</li>
<li>
<a href="#troubleshooting">Troubleshooting</a>
</li>
<li>
<a href="#developing">Developing</a>
</li>
<li>
<a href="#what is topic modeling">What is topic modeling?</a>
</li>
<li>
<a href="#what is dariah-de">What is DARIAH-DE?</a>
</li>
<li>
<a href="#license">License</a>
</li>
</ul>
<h2 id="getting started">Getting started</h2>
<p>Windows, macOS and Linux 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>You can also use the developer version (if you are not on Windows):</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>pip 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>
<br>
<div>
<h4 class="highlight center" style="padding-bottom: 0px; text-align: left;">Note</h4>
<p class="highlight center" style="text-align: left;">If you want to use the sample corpus, you
must clone the
repository with Git (<strong>not</strong> download the source code from the
<strong>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. If you download one of the archives (except the source code) from the
release section,
the corpus is included.</p>
</div>
<br />
<h2 id="application">The application</h2>
<img style="width: 800px; margin: 0 auto;" alt="Screenshot of DARIAH Topics Explorer" src="assets/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/notebooks/IntroducingMallet.ipynb">topic
modeling with MALLET</a>.</p>
<h2 id="visualization">Example visualization</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.</p>
<h2 id="troubleshooting">Troubleshooting</h2>
<ul class="simple">
<li>If you are confronted with
<strong>any problems</strong> regarding the application, use
<strong>
<a class="reference external" href="https://github.com/DARIAH-DE/TopicsExplorer/issues">GitHub
issues</a>
</strong> – but suggestions for improvements, wishes, or hints on typos are of course also
welcome.</li>
<li>Please be patient. Depending on corpus size and number of iterations, the process
<strong>may take some time</strong>, meaning something between some seconds and some hours.</li>
<li>If the program displays an
<strong>error message at startup</strong>, make sure that you have
<strong>unpacked</strong> the archive.</li>
<li>If you are on a
<strong>Mac</strong> and get an error message saying that the file is from an
<strong>“unidentified developer”</strong>, you can override it by holding control while
double-clicking. The
error message
will still appear, but you will be given an option to run the file anyway.</li>
<li>You might get a similar error message as the one above on
<strong>Windows</strong> systems:
<strong>“Windows Defender SmartScreen prevented an unrecognized app from starting”</strong>. If
this is the
case, please
select “More Info” and then “Run anyway”.</li>
<li>On a
<strong>Windows</strong> machine, if you are not able to start the program, if nothing happens
for a long time,
or if you
get an error message, go to the
<code>src</code> folder, search for the file
<code>webapp.exe</code> and click on it.</li>
<li>For more technical troubleshooting, have a look at the
<a class="reference external" href="https://github.com/DARIAH-DE/TopicsExplorer/blob/master/README.md">README</a>
on GitHub.</li>
</ul>
<h2 id="developing">Developing</h2>
<p>If you are interested in the more technical parts, using the development version in general, or even
want to
contribute,
please check out the
<a class="reference external" href="https://github.com/DARIAH-DE/TopicsExplorer#developing">GitHub</a>
page.</p>
<h2 id="about dariah">About DARIAH-DE</h2>
<p>
<a class="reference external" href="https://de.dariah.eu/">DARIAH-DE</a> supports research in the
humanities and
cultural sciences with digital methods and procedures. The research
infrastructure of DARIAH-DE consists of four pillars: teaching, research, research data and
technical
components.
As a partner in
<a class="reference external" href="http://dariah.eu/">DARIAH-EU</a>, DARIAH-DE helps to bundle and
network
state-of-the-art activities of the digital humanities. Scientists
use DARIAH, for example, to make research data available across Europe. The exchange of knowledge
and expertise
is
thus promoted across disciplines and the possibility of discovering new scientific discourses is
encouraged.</p>
<p>This application has been developed with support from the DARIAH-DE initiative, the German branch of
DARIAH-EU, the
European Digital Research Infrastructure for the Arts and Humanities consortium. Funding has been
provided by
the
German Federal Ministry for Research and Education (BMBF) under the identifier 01UG1610J.
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