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

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