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arXiv · 2207.00432

A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network

Chengjie Ma·Junping Du·Yingxia Shao·Ang Li·Zeli Guan
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Abstract

We provide a simple and general solution for the discovery of scarce topics in unbalanced short-text datasets, namely, a word co-occurrence network-based model CWIBTD, which can simultaneously address the sparsity and unbalance of short-text topics and attenuate the effect of occasional pairwise occurrences of words, allowing the model to focus more on the discovery of scarce topics. Unlike previous approaches, CWIBTD uses co-occurrence word networks to model the topic distribution of each word, which improves the semantic density of the data space and ensures its sensitivity in identify-ing rare topics by improving the way node activity is calculated and normal-izing scarce topics and large topics to some extent. In addition, using the same Gibbs sampling as LDA makes CWIBTD easy to be extended to vari-ous application scenarios. Extensive experimental validation in the unbal-anced short text dataset confirms the superiority of CWIBTD over the base-line approach in discovering rare topics. Our model can be used for early and accurate discovery of emerging topics or unexpected events on social platforms.

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Chengjie Ma, Junping Du, Yingxia Shao, Ang Li, Zeli Guan. 2022-06-30. A Rare Topic Discovery Model for Short Texts Based on Co-occurrence word Network. https://arxiv.org/abs/2207.00432

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