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), Our approach 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 we enhanced 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. Our 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.

我们提出了一种用于发现不均衡的短文本数据集中稀缺主题的简单解决方案，名为CWUTM（基于共现词网络的不均衡短文本数据集的主题模型）。该方法通过减轻偶然词共现的影响来解决稀疏和不均衡的短文本主题的挑战。与以前的方法不同，CWUTM利用共现词网络来捕捉每个单词的主题分布，并通过重新定义节点活动的计算和在一定程度上标准化稀缺和丰富主题的表示来增强对稀缺主题的敏感性。此外，CWUTM采用了类似于LDA的Gibbs抽样方法，使其能够轻松适应各种应用场景。我们在不均衡的短文本数据集上进行了广泛的实验证实，证明了CWUTM在发现稀缺主题方面相比基准方法的优越性。根据实验结果，所提出的模型在社交平台上能够及早、准确地检测出新兴主题或意外事件。

基于共现词网络的不平衡短文本主题模型