LLM-powered rephrasing boosts social media topic modeling accuracy

Researchers have developed TM-Rephrase, a novel framework designed to improve the topic modeling of short texts from social media platforms like X (formerly Twitter). This model-agnostic approach utilizes large language models to rephrase informal tweets into more standardized language before topic modeling is applied. A case study using COVID-19 related tweets demonstrated that TM-Rephrase enhances topic coherence, uniqueness, and diversity, with a colloquial-to-formal rephrasing strategy showing the most significant improvements, particularly for the Latent Dirichlet Allocation algorithm. AI

IMPACT Enhances the ability to extract meaningful insights from noisy social media data, improving public health analysis and discourse understanding.

RANK_REASON Academic paper detailing a new method for improving topic modeling using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• COVID-19
• latent Dirichlet allocation
• TM-Rephrase
• Wangjiaxuan Xin
• X

• paper
• other

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