An Evolutionary Context-aware Sequential Model for topic evolution of text stream

Ziyu Lu, Haihui Tan, Wenjie Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Social media acts as the platform for users to acquire information and spreads out breaking news. The overwhelming amount of fast-growing information makes it a challenge to track the subsequences of the breaking news or events and find the corresponding user opinions towards special aspects. Tracking the evolution of an event and predicting its subsequent trends play an important role in social media. In this paper, we propose an Evolutionary Context-aware Sequential model (ECSM) to track the evolutionary trends of the streaming text and investigate their focused context-aware topics. We integrate two novel layers into the Recurrent Chinese Restaurant Process (RCRP), respectively one context-aware topic layer and one Long Short Term Memory (LSTM) based sequential layer. The context-aware topic layer can help capture the global context-aware semantic coherences and the sequential layer is exploited to learn the local dynamics and semantic dependencies during the dynamic evolutionary process. Experimental results on real datasets show that our method significantly outperforms the state-of-the-art approaches.

Original languageEnglish
Pages (from-to)166-177
Number of pages12
JournalInformation Sciences
Volume473
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Evolutionary clustering
  • Long Short Term Memory
  • Recurrent Chinese Restaurant Process

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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