title: Python library for topic tagging / scoring of sentences with specific topic set
body: <p>I am looking for the best python library for achieving below NLP task. </p>
  
  <p>Given an example sentence, "he becomes king maha vajiralongkorn bodindradebayavarangkun the 10th monarch of the chakri dynasty but will also be known as rama x i would like to accept in order to fulfill his majestys wishes and for the benefit of all thais " (A sentence taken from this article <a href="http://www.bbc.com/news/world-asia-38168912" rel="nofollow noreferrer">http://www.bbc.com/news/world-asia-38168912</a>)</p>
  
  <p>I would like to compute topic scores for this sentences. For example, if I define my "topic set" to be {"politics", "tech", "business", "health", "entertainment"},</p>
  
  <p>I would like output such as: {"politics" : 0.9, "tech" : 0.1, "business": 0.1, "health" : 0.05, "entertainment" : 0.2}</p>
  
  <p>The key thing I am trying to achieve is that I would like to define my topic set and to have scores for topic in that set only. Also, sentences would be relatively short and I would like to compute outputs for sentences that come in streaming fashion. </p>
  
  <p>Thank you! </p>