Topic Detection Based on Semantics,Time and Social Relationship

摘要：

　　Short text sparsity,oral language,and polysemy are the main problems when dealing with social network data,which make the traditional methods hard to obtain the true meaning of social network data.Due to the above issues,topic detection for social network data is not that easy.And to solve the above problems,we propose an original Clustering Algorithm based on Semantics,Time,and Social relationship(CASTS)for topic detection.Firstly,to overcome short text sparsity and polysemy problems,the CASTS leverages the Bidirectional Encoder Representations from Transformers(BERT),which can pre-train on large-scale social network short text data to obtain concise text representation with rich semantics.Secondly,by combining the short text representation,time,and social relationship,the CASTS can efficiently detect topics.Finally,we conduct experiments on Weibo dataset to verify the correctness and effectiveness of CASTS.

关键词： Topic detection Semantics Time Social relationship

作者: Pengchao Cheng Junping Du Feifei Kou Zhe Xue Peihua Chen

作者单位: Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia,School of Computer S Xiaoi Research,Shanghai Xiaoi Robot Technology Co.,Ltd.,Shanghai 201803,China

会议类型: 国际会议

会议名称: 2019中国智能自动化大会(CIA,2019)

会议地点: 江苏镇江

会议语种:英文

页码: 691-698

在线出版日期: 2019-09-20（万方平台首次上网日期，不代表论文的发表时间）