code: def get_word_distinctiveness(topic_word_distrib, p_t):
      
      topic_given_w = topic_word_distrib / topic_word_distrib.sum(axis=0)
      return (topic_given_w * np.log(topic_given_w.T / p_t).T).sum(axis=0)
docs: Calculate word distinctiveness according to Chuang et al. 2012.
      distinctiveness(w) = KL(P(T|w), P(T)) = sum_T(P(T|w) log(P(T|w)/P(T)))
      with P(T) .. marginal topic distribution
           P(T|w) .. prob. of a topic given a word
  
      J. Chuang, C. Manning, J. Heer 2012: "Termite: Visualization Techniques for Assessing Textual Topic Models"