My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text Collections

TL;DR

This paper introduces an entropy-based cross-collection topic model that effectively distinguishes domain-specific and general words, improving coherence, perplexity, and classification accuracy across diverse text collections.

Contribution

The paper presents a novel entropy-based topic modeling approach that separates collection-specific and independent words, enhancing interpretability and performance in multi-collection text analysis.

Findings

Achieves up to 13% higher topic coherence

Achieves up to 4% lower perplexity

Achieves up to 31% higher classification accuracy

Abstract

Comparative text mining extends from genre analysis and political bias detection to the revelation of cultural and geographic differences, through to the search for prior art across patents and scientific papers. These applications use cross-collection topic modeling for the exploration, clustering, and comparison of large sets of documents, such as digital libraries. However, topic modeling on documents from different collections is challenging because of domain-specific vocabulary. We present a cross-collection topic model combined with automatic domain term extraction and phrase segmentation. This model distinguishes collection-specific and collection-independent words based on information entropy and reveals commonalities and differences of multiple text collections. We evaluate our model on patents, scientific papers, newspaper articles, forum posts, and Wikipedia articles. In comparison to state-of-the-art cross-collection topic modeling, our model achieves up to 13% higher topic coherence, up to 4% lower perplexity, and up to 31% higher document classification accuracy. More importantly, our approach is the first topic model that ensures disjunct general and specific word distributions, resulting in clear-cut topic representations.