This is a research project which has 3 parts:
- clustering/xpath based content extractor
- LDA topic modeling
- AngularMaterial based web app which serves the user requests and shows the extracted content via different explorative techniques
balbla demo task
//*/div will extract all div elements in the document while a more specific path will be the actual conent path. The idea is to include number of elements which corespond to an xpath to the evaluation of the ratio. Question is how (e.g. some logaritmic or similar range?)
Create web app with Flask (no nginx for now) which will show 20 topics and the articles which belong to the topics.
*Flask as web server
1 http://flask.pocoo.org/docs/0.10/quickstart/
*POLYMER OR ANGULAR FOR FORNTEND
1 https://www.polymer-project.org/1.0/
2 https://material.angularjs.org/latest/
3 dummy text for this task
Create HTML forntend for web app
Currently only minimal text preparation exists, based on NLTK stopwords and tokenizer.
TO DO:
To try:
List of Py Interfaces for Stanfrods NER:
http://nlp.stanford.edu/software/corenlp.shtml
To show:
20 topics
for each topic top N keywords
latest M articles which fit best to that category
TO do for Angular App:
Currently, a few objects are creating their own MySQL objects and are executing MySQL queries wihch can lead to diminished preformances. try to fix that and exclude incremental inclusions of libraries which are in common to specific parts of the API.
http://lxml.de/api/lxml.etree._ElementTree-class.html#getpath
Implement LXML getpath function to extract xpath values of each node.
IMPORTANT
parsing the text using LXML and its iterative proces has major issues (e.g. skipping all elements in the text). try to convert LXML element to HTML and use BeautifulSoup to extract text.
Update gensim models with new documents every N minutes (hours). Read the create date of LDA model files and get all articles from DB younger than that date. Add new documents to the model and attach each to one of 20 categories in the generated gensim LDA model.
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