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

arXiv: https://arxiv.org/abs/1904.07695 · HTML: https://mlsanity.com/paper/1904.07695/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2019.

## Built on by (most cited first, 30 of 38)

- [LLM-Rec: Personalized Recommendation via Prompting Large Language Models](https://mlsanity.com/paper/2307.15780/) (2023)
- [Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning](https://mlsanity.com/paper/2211.12878/) (2022)
- [Prompting Large Language Models for Topic Modeling](https://mlsanity.com/paper/2312.09693/) (2023)
- [Towards the TopMost: A Topic Modeling System Toolkit](https://mlsanity.com/paper/2309.06908/) (2023)
- [A Survey on Neural Topic Models: Methods, Applications, and Challenges](https://mlsanity.com/paper/2401.15351/) (2024)
- [An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages](https://mlsanity.com/paper/2109.00165/) (2021)
- [Happenstance: Utilizing Semantic Search to Track Russian State Media Narratives about the Russo-Ukrainian War On Reddit](https://mlsanity.com/paper/2205.14484/) (2022)
- [Diversity-Aware Coherence Loss for Improving Neural Topic Models](https://mlsanity.com/paper/2305.16199/) (2023)
- [Improving Contextualized Topic Models with Negative Sampling](https://mlsanity.com/paper/2303.14951/) (2023)
- [GINopic: Topic Modeling with Graph Isomorphism Network](https://mlsanity.com/paper/2404.02115/) (2024)
- [GloCOM: A Short Text Neural Topic Model via Global Clustering Context](https://mlsanity.com/paper/2412.00525/) (2024)
- [Short Text Clustering with Transformers](https://mlsanity.com/paper/2102.00541/) (2021)
- [Deep Forest with Hashing Screening and Window Screening](https://mlsanity.com/paper/2207.11951/) (2022)
- [Human-interpretable clustering of short-text using large language models](https://mlsanity.com/paper/2405.07278/) (2024)
- [BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study](https://mlsanity.com/paper/2501.03843/) (2025)
- [OPP-Miner: Order-preserving sequential pattern mining](https://mlsanity.com/paper/2202.03140/) (2022)
- [No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling](https://mlsanity.com/paper/2208.01712/) (2022)
- [iGAiVA: Integrated Generative AI and Visual Analytics in a Machine Learning Workflow for Text Classification](https://mlsanity.com/paper/2409.15848/) (2024)
- [Short Text Topic Modeling: Application to tweets about Bitcoin](https://mlsanity.com/paper/2203.11152/) (2022)
- [ProSiT! Latent Variable Discovery with PROgressive SImilarity Thresholds](https://mlsanity.com/paper/2210.14763/) (2022)
- [Reinforcement Learning for Topic Models](https://mlsanity.com/paper/2305.04843/) (2023)
- [Towards Generalising Neural Topical Representations](https://mlsanity.com/paper/2307.12564/) (2023)
- [A Contextual Topic Modeling and Content Analysis of Iranian laws and Regulations](https://mlsanity.com/paper/2309.13051/) (2023)
- [Recommending the right academic programs: An interest mining approach using BERTopic](https://mlsanity.com/paper/2501.06581/) (2025)
- [S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling](https://mlsanity.com/paper/2507.12451/) (2025)
- [LLM-Assisted Topic Reduction for BERTopic on Social Media Data](https://mlsanity.com/paper/2509.19365/) (2025)
- [Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling](https://mlsanity.com/paper/2108.06862/) (2021)
- [A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network](https://mlsanity.com/paper/2207.00432/) (2022)
- [Demystifying the COVID-19 vaccine discourse on Twitter](https://mlsanity.com/paper/2208.13523/) (2022)
- [Multidimensional Service Quality Scoring System](https://mlsanity.com/paper/2212.04611/) (2022)

## Abstract (arXiv)

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.