Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2010

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.

Keywords

Opinion mining, Sentiment analysis, PLSA

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

Knowledge and Information Systems

Volume

27

Issue

2

First Page

281

Last Page

302

ISSN

0219-1377

Identifier

10.1007/s10115-010-0325-9

Publisher

Springer

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1007/s10115-010-0325-9

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