Mining opinion summarizations using convolutional neural networks in Chinese microblogging systems

Qiudan Li, Zhipeng Jin, Can Wang, Dajun Zeng

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

Chinese microblogging is an increasingly popular social media platform. Accurately summarizing representative opinions from microblogs can increase understanding of the semantics of opinions. The unique challenges of Chinese opinion summarization in microblogging systems are automatic learning of important features and selection of representative sentences. Deep-learning methods can automatically discover multiple levels of representations from raw data instead of requiring manual engineering. However, there have been very few systematic studies on sentiment analysis of Chinese hot topics using deep-learning methods. Based on the latest deep-learning research, in this paper, we propose a convolutional neural network (CNN)-based opinion summarization method for Chinese microblogging systems. The model first applies CNN to automatically mine useful features and perform sentiment analysis; then, by making good use of the obtained sentiment features, the semantic relationships among features are computed according to a hybrid ranking function; and finally, representative opinion sentences that are semantically related to the features are extracted using Maximal Marginal Relevance, which meets “relevant novelty” requirements. Experimental results on two real-world datasets verify the efficacy of the proposed model.

Original languageEnglish (US)
Pages (from-to)289-300
Number of pages12
JournalKnowledge-Based Systems
Volume107
DOIs
StatePublished - Sep 1 2016

Keywords

  • Chinese microblogging systems
  • Convolutional neural network
  • Hot topics
  • Maximal marginal relevance
  • Opinion summarization

ASJC Scopus subject areas

  • Management Information Systems
  • Software
  • Information Systems and Management
  • Artificial Intelligence

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