Merge branch 'testing'

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/README.md

@@ -4,17 +4,27 @@ This web application introduces an user-friendly workflow, basically containing
 
 ![Demonstrator Screenshot](screenshot.png)
 
+
 ## First steps
 
-**Important**: Please make sure all dependencies are properly installed, including the `dariah_topics` module. If not (or you are not sure), simply run `pip install -r requirements.txt` (or `pip3 install -r requirements.txt` if you are on an [UNIX-based](https://en.wikipedia.org/wiki/Unix) operating system like macOS or Linux Ubuntu) through the [command-line](https://en.wikipedia.org/wiki/Command-line_interface) within `Topics`.
+**Only Linux**: Please make sure all dependencies are properly installed, including the `dariah_topics` module. If not (or you are not sure), simply run `pip3 install -r requirements.txt` in the [command-line](https://en.wikipedia.org/wiki/Command-line_interface) within `Topics`.
 
 ### Running the application
-To run the application, type `python demonstrator.py` (or `python3 demonstrator.py` for UNIX) in the command-line and press enter. Your default browser should immediately display the interface (it might take some seconds until your browser automatically opens – if not, do it by yourself and go to `http://127.0.0.1:5000`).<br>
-
-**Important**: This application aims for simplicity and usability. If you are working with a large corpus (> 200 documents) you may wish to use more sophisticated topic models such as those implemented in MALLET, which is known to be more robust than standard LDA. Have a look at our Jupyter notebook [introducing topic modeling with MALLET](https://github.com/DARIAH-DE/Topics/blob/testing/IntroducingMallet.ipynb).<br>
+This application aims for simplicity and usability. If you are working with a large corpus (> 200 documents) you may wish to use more sophisticated topic models such as those implemented in MALLET, which is known to be more robust than standard LDA. Have a look at our Jupyter notebook [introducing topic modeling with MALLET](https://github.com/DARIAH-DE/Topics/blob/testing/IntroducingMallet.ipynb).<br>
 
 **Hint**: To gain better results, it is highly recommended to use one of the provided [stopword lists](https://github.com/DARIAH-DE/Topics/blob/master/tutorial_supplementals/stopwords). Removing the most frequent words is a dangerous game, because you might remove quite important words.
 
+#### Windows and macOS
+Although this application is built with Python, it is possible to run it as if it was a native application, without having to install Python or any related packages. There is currently one build for Windows and macOS, respectively.
+
+1. Download `demonstrator-0.0.1-windows.zip` or `demonstrator-0.0.1-mac.zip` from the [release-section](https://github.com/DARIAH-DE/Topics/releases).
+2. Open it by double-clicking.
+3. Run the app by double-clicking.
+4. **Mac**: If you get an error message saying that the file is from an “unidentified developer”, 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.
+
+#### Linux
+To run the application, type `python3 demonstrator.py` in the command-line and press enter. Your default browser should immediately display the interface (it might take some seconds until your browser automatically opens – if not, do it by yourself and go to `http://127.0.0.1:5000`).
+
 ### Handling the application
 The application behaves just like any other website. Basically, there are only two sites: one to select text files and make some more adjustments, and one to show what your topic model has generated. Once clicked the `Send`-button, all generated data will be stored in the cache and you can jump between the pages without losing any data. **But be careful**, once you clicked the `Send`-button again, all of the previous data will be lost.
 
@@ -26,16 +36,6 @@ If you are confronted with any issues, please use `Issues` [on GitHub](https://g
 - In case you want to jump from the output site back to the first page, but your browser displays a blank page, press the reload button. Jumping between sites should be possible within seconds, in any other cases something went wrong.
 - If you get a `ModuleNotFoundError`-error, your dependencies are probably not up-to-date. Try running `pip install -r requirements.txt` (or `pip3 install -r requirements.txt` for UNIX) in the command-line within `Topics`.
 
-
-## Stand-alone application
-Although this application is built with Python, it is possible to run it as if it was a native application, without having to install Python or any related packages. There is currently one build for Windows and macOS, respectively.
-
-### Running the stand-alone application
-1. Download `demonstrator-0.0.1-windows.zip` or `demonstrator-0.0.1-mac.zip` from the [release-section](https://github.com/DARIAH-DE/Topics/releases/tag/0.0.1).
-2. Open it by double-clicking.
-3. Run the app by double-clicking.
-4. **Mac**: If you get an error message saying that the file is from an “unidentified developer”, 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.
-
 ## Creating a build
 To create a stand-alone application, you need to install `pyinstaller` and run:
 

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