Topic Analysis of Social Media Hotlists Enhanced by Large Language Models
A variety of hot topics lists released by social media platforms serve as a convergence and showcase for hot topic information,which provides significant in-sights toward our understanding of current popular discussions.However,due to vocabulary sparsity and short text length in hot list texts,traditional LDA and neu-ral network-based topic mining models face poor performance in topic aggregation.To address these challenges,the paper proposes a topic modeling framework enhanced by a large language model—STAB,which combines the generative capabilities of large language models for text data with the excellent performance of document em-beddings in topic modeling,enabling the extraction of meaningful topics from short text datasets.Experimental results on multiple datasets show that our framework outperforms existing topic modeling methods in terms of general objective evaluation metrics and applications in downstream tasks.
Social media analysistopic modelingshort text topic modelinglarge language modelsdata augmentationpublic sentiment analysis
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