We describe a new method for visualizing topics, the distributions over terms that are automatically extracted from large text corpora using latent variable models. Our method finds significant n-grams related to a topic, which are then used to help understand and interpret the underlying distribution. Compared with the usual visualization, which simply lists the most probable topical terms, the multi-word expressions provide a better intuitive impression for what a topic is "about." Our approach is based on a language model of arbitrary length expressions, for which we develop a new methodology based on nested permutation tests to find significant phrases. We show that this method outperforms the more standard use of χ 2 and likelihood ratio tests. We illustrate the topic presentations on corpora of scientific abstracts and news articles.
Aiming to help users understand topics clearly, Blei and Lafferty REF described a method for visualizing topics extending LDA model to generate the multi-word distribution of topics.
Visualizing Topics with Multi-Word Expressions
arXiv: Machine Learning
Computer Science Mathematics