Merge branch 'testing' of https://github.com/DARIAH-DE/Topics into te…

…sting

README.md

@@ -1,6 +1,6 @@
 # Topics – Easy Topic Modeling in Python
 
-[Topics](http://dev.digital-humanities.de/ci/job/DARIAH-Topics/doclinks/1/) is a Python library for Text Mining and Topic Modeling. Furthermore, this repository provides a convenient, modular workflow that can be entirely controlled from within and which comes with a well documented [Jupyter](http://jupyter.org/) notebook. Users not yet familiar with programming in Python can test basic Topic Modeling in a [Flask](http://flask.pocoo.org/)-based [GUI demonstrator](/demonstrator/README.md). For a standalone application, which does not require a Python interpreter or any extra installations, have a look at the [release-section](https://github.com/DARIAH-DE/Topics/releases).
+[Topics](http://dev.digital-humanities.de/ci/job/DARIAH-Topics/doclinks/1/) is a Python library for Text Mining and Topic Modeling. Furthermore, this repository provides a convenient, modular workflow that can be entirely controlled from within and which comes with a well documented [Jupyter](http://jupyter.org/) notebook. Users not yet familiar with programming in Python can test basic Topic Modeling in a [Flask](http://flask.pocoo.org/)-based [GUI demonstrator](/demonstrator/README.md). **For a standalone application**, which does not require a Python interpreter or any extra installations, **have a look at the [release-section](https://github.com/DARIAH-DE/Topics/releases)**.
 
 At the moment, this library supports three LDA implementations:
 * [lda](http://pythonhosted.org/lda/index.html), which is lightweight and provides basic LDA.
@@ -11,7 +11,7 @@ At the moment, this library supports three LDA implementations:
 * [Topics website](http://dev.digital-humanities.de/ci/job/DARIAH-Topics/doclinks/1/)
 * [Topics API documentation](http://dev.digital-humanities.de/ci/job/DARIAH-Topics/doclinks/1/docs/gen/modules.html)
 * [Topics paper](https://dh2017.adho.org/abstracts/411/411.pdf)
-* [Demonstrator releases](https://github.com/DARIAH-DE/Topics/releases)
+* [Standalone Demonstrator releases](https://github.com/DARIAH-DE/Topics/releases)
 * [An introduction to Topic Modeling using lda](IntroducingLda.ipynb)
 * [An introduction to Topic Modeling using MALLET](IntroducingMallet.ipynb)
 * [An introduction to Topic Modeling using Gensim](IntroducingGensim.ipynb)

demonstrator/templates/result.html

@@ -113,9 +113,7 @@ <h3>1.1. Topics</h3>
               {% for table in topics %} {{ table|safe }} {% endfor %}
               <br>
               <h3>1.2. Topics in documents</h3>
-              <p>Each topic has a certain probability for each document in the corpus. This probability distributions are visualized in an interactive <b>heatmap</b> (the darker the color, the higher the probability) which displays the kind of information
-                that is presumably most useful to literary scholars. Going beyond pure exploration, this visualization can be used to show thematic developments over a set of texts as well as a single text, akin to a dynamic topic model. What might become
-                apparent here, is that some topics correlate highly with a specific author or group of authors, while other topics correlate highly with a specific text or group of texts.</p><br>
+              <p>The heatmap option displays the kind of information that is probably most useful to literary scholars. Going beyond pure exploration, this visualization can be used to show thematic developments over a set of texts as well as a single text, akin to a dynamic topic model. What also can become apparent here, is that some topics correlate highly with a specific author or group of authors, while other topics correlate highly with a specific text or group of texts. All in all, this displays two of LDA's properties - its use as a distant reading tool that aims to get at text meaning, and its use as a provider of data that can be further used in computational analysis, such as document classification or authorship attribution.</p><br>
               {{ div|safe }}<br>
               <h2>2. Getting deeper into topic modeling</h2>
               <p>We want to empower users with little or no previous experience and programming skills to create custom workflows mostly using predefined functions within a familiar environment. So, if this practical introduction aroused your interest and