Extracting common emotions from blogs based on fine-grained sentiment clustering |
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Authors: | Shi Feng Daling Wang Ge Yu Wei Gao Kam-Fai Wong |
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Affiliation: | (1) Department of Computer Science and Engineering, Indian Institute of Technology, Chennai, 600036, India;(2) General Motors, R&D, India Science Lab, Bangalore, 560066, India |
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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. |
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