text: Title: Topic Saliency and Relevancy Frequency Distribution 
  Body: **Describe the solution you'd like**
  Adapt or extend the `FreqDist` visualizer to show not just the frequency of tokens in the corpus but rank them according to their relationship to a topic model (e.g. LDA). Ranked by *saliency*, the `FreqDist` will show the frequency of the terms that contribute the most information to all topics. Ranked by *relevance* the `FreqDist` will show the terms most relevant to a specific topic and the proportion of their frequency in the topic compared to the rest of the corpus. Metric definitions are:
  
  1. **saliency(term w)** = frequency(w) * [sum_t p(t | w) * log(p(t | w)/p(t))] for topics t; see [Chuang et. al (2012)](http://vis.stanford.edu/files/2012-Termite-AVI.pdf)
  2. **relevance(term w | topic t)** = λ * p(w | t) + (1 - λ) * p(w | t)/p(w); see [Sievert & Shirley (2014)](http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf)
  
  This computation requires a model (so probably our best bet is to extend `FreqDist` to `TopicFreqDist` and make it a model visualizer). It also rquires a parameter, lambda. 
  
  **Is your feature request related to a problem? Please describe.**
  Implementing this will bring YB closer to being able to provide a PyLDAViz-like solution to topic modeling and clustering. 
  
  **Examples**
  
  Here is the `FreqDist` ranked by saliency to a topic model instead of ranked purely by freqency:
  
  <img width="623" alt="screenshot 2018-08-21 06 47 13" src="https://user-images.githubusercontent.com/745966/44397360-1a0d3b00-a50e-11e8-94bc-e684dd743ff6.png">
  
  Here is a topic's terms ranked by relevance and compared to the rest of the corpus (e.g. red vs. blue):
  
  <img width="635" alt="screenshot 2018-08-21 06 48 56" src="https://user-images.githubusercontent.com/745966/44397420-4923ac80-a50e-11e8-9991-84452a30384f.png">
  
  