Detecting Topics of Group Chats with Multiple Strategies
Wu Xu1,2,3(),Chen Chunxu1,2
1Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing 100876, China 2School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China 3Beijing University of Posts and Telecommunications Library, Beijing 100876, China
[Objective] This paper tries to detect topics of continuous group chats with variou types of message, aiming to address the topic entanglement issue of group chats, and reduce the influence of sparse text features on clustering. [Methods] We proposed a detection model for group chat topics based on multi-strategies. This model solves topic crossover issue with topic sequences, and improves clustering results with data on users, time, and types of messages. [Results] We examined our model with plain texts of three group chats. The new method’s F value was 2.9%, 6.1% and 3.0% higher than those of the existing algorithms. The speed of our model is about 27.6%, 32.1% and 47.1% faster. This method also processed mixed types of data that cannot be handled by traditional algorithms, and the speed was improved by about 29.4%, 27.1%, and 22.5% respectively. [Limitations] We do not fully utilize the text features of group chat message and set too many thresholds for the algorithm. [Conclusions] The proposed method could identify group chat topics, and improve the efficiency of public opinion analysis.
吴旭,陈春旭. 基于多策略的群聊话题检测技术*[J]. 数据分析与知识发现, 2021, 5(5): 1-9.
Wu Xu,Chen Chunxu. Detecting Topics of Group Chats with Multiple Strategies. Data Analysis and Knowledge Discovery, 2021, 5(5): 1-9.
(China Internet Network Information Center. The 44th China Statistical Report on Internet Development[R/OL].(2019-08-30).[2020-04-10]. https://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201908/t20190830_70800.htm.)
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