Follow along –

What is a topic model?

  • Topic models discover 'topics' that occur in a collection of text:
  • Statistics may be dull, but it has its moments.
  • 67% topic A, 33% topic B.
  • Please laugh.
  • 50% topic B, 50% topic C.
  • Laughing is good.
  • 100% topic C.

Towards topic interpretation

  • Each topic owns a different probability mass function over the same set of words (i.e. vocabulary).
  • Problem: Topics are not easily interpretable and vocabulary size is often very large. Where should we put our focus?
  • Typically, one produces a ranked list of words deemed important for understanding a given topic; but how should we measure importance?
  • Measure 1: \(p(w_i|z_j)\) – probability of word \(w_i\) given each topic \(z_j\).
  • Drawback: common words tend to appear near the top of such lists for multiple topics, making it hard to differentiate topics.
  • Measure 2: \(\text{lift} = \frac{p(w_i|z_j)}{p(w_i)}\) where \(p(w_i)\) is overall probability of word \(w_i\).
  • Drawback: Rare words tend to receive too high of a ranking.
  • We believe that a compromise between these two measures can aid topic interpretation: \[ \text{relevance} = \lambda * p(w_i|z_j) + (1 - \lambda) * \text{lift} \]

User study

A few remarks

  • We anticipate this 'optimal' value of \(\lambda\) will vary for different datasets.
  • For this reason, it is nice to have an interactive tool that quickly iterates through word rankings (based on different values of \(\lambda\)).
  • The R package LDAvis makes it easy to create an interactive visualizations to aid topic interpretation.

Some links