Extracting common emotions from blogs based on fine-grained sentiment clustering

Shi Feng, Daling Wang, Ge Yu, Wei Gao, Kam Fai Wong

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people's collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people's collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In this paper, a novel method based on Probabilistic Latent Semantic Analysis (PLSA) is presented to model the hidden sentiment factors and an emotion-oriented clustering approach is proposed to find common emotions according to the fine-grained sentiment similarity between blogs. Extensive experiments are conducted on real-world datasets consisting of different topics. The results show that our approach can partition blogs into sentiment coherent clusters and the extracted common emotion words afford good navigation guidelines for embedded sentiments in each cluster.

Original languageEnglish
Pages (from-to)281-302
Number of pages22
JournalKnowledge and Information Systems
Volume27
Issue number2
DOIs
Publication statusPublished - 1 May 2011

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Keywords

  • Opinion mining
  • PLSA
  • Sentiment analysis

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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