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We propose a straightforward solution for detecting scarce topics inunbalanced short-text datasets. Our approach, named CWUTM (Topic model based onco-occurrence word networks for unbalanced short text datasets), Our approachaddresses the challenge of sparse and unbalanced short text topics bymitigating the effects of incidental word co-occurrence. This allows our modelto prioritize the identification of scarce topics (Low-frequency topics).Unlike previous methods, CWUTM leverages co-occurrence word networks to capturethe topic distribution of each word, and we enhanced the sensitivity inidentifying scarce topics by redefining the calculation of node activity andnormalizing the representation of both scarce and abundant topics to someextent. Moreover, CWUTM adopts Gibbs sampling, similar to LDA, making it easilyadaptable to various application scenarios. Our extensive experimentalvalidation on unbalanced short-text datasets demonstrates the superiority ofCWUTM compared to baseline approaches in discovering scarce topics. Accordingto the experimental results the proposed model is effective in early andaccurate detection of emerging topics or unexpected events on social platforms.

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