Visible to the public Topic Evolution Modeling in Social Media Short Texts Based on Recurrent Semantic Dependent CRP

TitleTopic Evolution Modeling in Social Media Short Texts Based on Recurrent Semantic Dependent CRP
Publication TypeConference Paper
Year of Publication2017
AuthorsZhang, Y., Mao, W., Zeng, D.
Conference Name2017 IEEE International Conference on Intelligence and Security Informatics (ISI)
Date Publishedjul
KeywordsAdaptation models, Analytical models, composability, Data models, HDP, Human Behavior, human factors, LDA, Media, Metrics, pubcrawl, recurrent semantic dependent CRP, rsdCRP, Scalability, sdTEM, semantic dependent Chinese restaurant process, semantic similarity information, Semantics, short-text oriented topic evolution model, Social Media Analytics, social media short texts, Social network services, social networking (online), text analysis, text analytics, text mining, Tools, topic modeling, Twitter dataset, word co-occurrence modeling

Social media has become an important platform for people to express opinions, share information and communicate with others. Detecting and tracking topics from social media can help people grasp essential information and facilitate many security-related applications. As social media texts are usually short, traditional topic evolution models built based on LDA or HDP often suffer from the data sparsity problem. Recently proposed topic evolution models are more suitable for short texts, but they need to manually specify topic number which is fixed during different time period. To address these issues, in this paper, we propose a nonparametric topic evolution model for social media short texts. We first propose the recurrent semantic dependent Chinese restaurant process (rsdCRP), which is a nonparametric process incorporating word embeddings to capture semantic similarity information. Then we combine rsdCRP with word co-occurrence modeling and build our short-text oriented topic evolution model sdTEM. We carry out experimental studies on Twitter dataset. The results demonstrate the effectiveness of our method to monitor social media topic evolution compared to the baseline methods.

Citation Keyzhang_topic_2017