# Topic model based on co-occurrence word networks for unbalanced short text datasets

arXiv: https://arxiv.org/abs/2311.02566 · HTML: https://mlsanity.com/paper/2311.02566/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Self-Supervised Graph Co-Training for Session-based Recommendation](https://mlsanity.com/paper/2108.10560/) (2021)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation](https://mlsanity.com/paper/2212.12738/) (2022)
- [Context Reinforced Neural Topic Modeling over Short Texts](https://mlsanity.com/paper/2008.04545/) (2020)
- [Word Network Topic Model: A Simple but General Solution for Short and Imbalanced Texts](https://mlsanity.com/paper/1412.5404/) (2014)
- [Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning](https://mlsanity.com/paper/2205.00256/) (2022)
- [STTM: A Tool for Short Text Topic Modeling](https://mlsanity.com/paper/1808.02215/) (2018)

## Abstract (arXiv)

We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets. Our approach, named CWUTM (Topic model based on co-occurrence word networks for unbalanced short text datasets), addresses the challenge of sparse and unbalanced short text topics by mitigating the effects of incidental word co-occurrence. This allows our model to prioritize the identification of scarce topics (low-frequency topics). Unlike previous methods, CWUTM leverages co-occurrence word networks to capture the topic distribution of each word, and enhances the sensitivity in identifying scarce topics by redefining the calculation of node activity and normalizing the representation of both scarce and abundant topics to some extent. Moreover, CWUTM adopts Gibbs sampling, similar to LDA, making it easily adaptable to various application scenarios. Extensive experimental validation on unbalanced short-text datasets demonstrates the superiority of CWUTM compared to baseline approaches in discovering scarce topics. According to the experimental results, the proposed model is effective in early and accurate detection of emerging topics or unexpected events on social platforms.