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

By Jipeng Qiang, Qian Zhenyu, Li Yun, Yuan Yunhao, Wu Xindong. IEEE Transactions on Knowledge and Data Engineering, 2019

307 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/2c53c86f7bb1c3c2a29af6d1b59a30dade425258

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In Artificial Intelligence

1. Roman Egger
2. Apurva Shah
3. Joanne Yu
4. Uttam Chauhan

Citations in the corpus

• A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts (2022, background): “Although several recent studies have vouched for the exploration of short-text social media data (Albalawi et al., 2020; Qiang et al., 2020), existing knowledge is rather restricted to conventional modeling techniques such as LDA and LSA (Albalawi et al., 2020).”
• Topic Modeling Using Latent Dirichlet allocation (2021, background): “surveyed over the various short text topics models and displayed the comparison of their performance in different scenarios [110].”