Ensemble Topic Modeling using Weighted Term Co-associations

Topic modeling is a popular unsupervised technique that is used to discover the latent thematic structure in text corpora. The evaluation of topic models typically involves measuring the semantic coherence of the terms describing each topic, where a single value is used to summarize the quality of an overall model. However, this can create difficulties when one seeks to interpret the strengths and weaknesses of a given topic model. With this in mind, we propose a new ensemble topic modeling approach that incorporates both stability information, in the form of term co-associations, and semantic similarity information, as derived from a word embedding constructed on a background corpus. Our evaluations show that this approach can simultaneously yield higher quality models when considering the produced topic descriptors and document-topic assignments, while also facilitating the comparison and evaluation of solutions through the visualization of the discovered topical structure, the ordering of the topic descriptors, and the ranking of term pairs which appear in topic descriptors.