title: Stanford CoreNLP Sentiment Analysis with Text Classification
body: <p>I am working on my individual bachelor's degree final project which is due in about 50 days. The website that I'm looking to create is one where users can share links to articles that they find amusing (something that makes them happy). It's a reddit like format where users can post globally and others can vote up or down articles based on how happy it makes them. The top trending posts will be shown at the top of the list and the least popular at the bottom.</p>
  
  <p>The more exciting part of the project is implementing an NLP machine learning service that crawls the web for articles similar to the top trending ones and automatically posting articles to the website (without user input apart from the voting). In order to do this, I was thinking about having a Stanford CoreNLP service running on the server that picked out the the top trending articles, classified them based on what they're about (e.g. an article on Donald Trump should automatically generate tags such as 'Donald Trump', 'Republican', 'politics', etc.) Then by carrying out sentiment analysis on the article, using the Stanford CoreNLP sentiment annotator, I could see what the public's opinion is on the topics of the article (i.e. the tags). Then by using a web crawler, extracting articles from the web, and carrying out similar sentiment analysis on the articles extracted, I can find suitable articles to post to the website.</p>
  
  <p>However, I haven't been able to find any annotators for text classification in Stanford CoreNLP. Is there any way I can implement what I have in mind. Better yet, are there any better ways of carrying out what I'm looking to achieve.</p>
  
  <p>Thanks in advance.</p>