Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (6A): 240700105-8.doi: 10.11896/jsjkx.240700105

• Artificial Intelligence • Previous Articles     Next Articles

Study on Algorithm for Keyword Extraction from WeChat Conversation Text

WANG Baohui1, XU Boren2, LI Chang’ao1, YE Zihao1   

  1. 1 College of Software,Beihang University,Beijing 100191,China
    2 School of Computing,Beihang University,Beijing 100191,China
  • Online:2025-06-16 Published:2025-06-12
  • About author:WANG Baohui,born in 1973,professor,master supervisor.His main research interests include network security,big data,artificial intelligence,etc.

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Abstract: WeChat group chats contain a large volume of conversational text data,and extracting keywords from these conversations helps to understand group dynamics and topic evolution.Traditional keyword extraction methods perform poorly due to the characteristics of WeChat conversations,such as short length,topic interleaving,and informal language use.To address these challenges,this paper proposes a multi-stage keyword extraction algorithm based on conversation topic clustering.First,we introduce a conversation topic clustering algorithm(single pass using thread segmentation and pre-training knowledge,SPTSPK),addressing the issues of topic interleaving and insufficient information by comprehensively considering semantic relevance,message activityand user intimacy.Second,we propose a multi-stage keyword extraction algorithm(MSKE) that decomposes the task into unsupervised keyword extraction and supervised keyword generation to extract both present and absent keywords from the original text,reducing the scale of candidate words and semantic redundancy.Finally,we conbine SPTSPK with MSKE to achieve keyword extraction from WeChat conversation texts.Compared to AutoKeyGen on the WeChat dataset,average F1@5 and F1@O increase by 12.8% and 10.8% respectively,and average R@10 reaches 2.59 times.Experimental results show that the proposed algorithm can effectively extract keywords from WeChat conversation texts.

Key words: Text clustering, Text generation, Conversation topic clustering, Keyword extraction

CLC Number: 

  • TP391
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