Hands-On Machine Learning for Algorithmic Trading

How to visualize LDA results using pyLDAvis

Topic visualization facilitates the evaluation of topic quality using human judgment. pyLDAvis is a Python port of LDAvis, developed in R and D3.js. We will introduce the key concepts; each LDA implementation notebook contains examples.

pyLDAvis displays the global relationships between topics while also facilitating their semantic evaluation by inspecting the terms most closely associated with each topic and, inversely, the topics associated with each term. It also addresses the challenge that terms that are frequent in a corpus tend to dominate the multinomial distribution over words that define a topic. LDAVis introduces the relevance r of the term w to topic t, to produce a flexible ranking ...