Short Text Topic Modeling Techniques, Applications, and Performance: A Survey

TL;DR

This survey reviews recent techniques for short text topic modeling, highlighting their categories, performance, and introduces an open-source library to facilitate further research and development in this area.

Contribution

It provides a comprehensive classification of short text topic modeling methods, introduces the first open-source library, and benchmarks various algorithms on real-world datasets.

Findings

Different methods show varying effectiveness depending on the dataset.

The open-source library STTM integrates multiple algorithms for easy comparison.

Short text models outperform traditional long text models on short text datasets.

Abstract

Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm.