Propagation-based sentiment analysis for microblogging data

Jiliang Tang, Chikashi Nobata, Anlei Dong, Yi Chang, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

The explosive popularity of microblogging services encourages more and more online users to share their opinions, and sentiment analysis on such opinion-rich resources has been proven to be an effective way to understand public opinions. On the one hand, the brevity and informality of microblogging data plus its wide variety and rapid evolution of language in microblogging pose new challenges to the vast majority of existing methods. On the other hand, microblogging texts contain various types of emotional signals strongly associated with their sentiment polarity, which brings about new opportunities for sentiment analysis. In this paper, we investigate propagation-based sentiment analysis for microblogging data. In particular, we provide a propagating process to incorporate various types of emotional signals in microblogging data into a coherent model, and propose a novel sentiment analysis framework PSA which learns from both labeled and unlabeled data by iteratively alternating a propagating process and a fitting process. We conduct experiments on real-world microblogging datasets, and the results demonstrate the effectiveness of the proposed framework. Further experiments are conducted to probe the working of the key components of the proposed framework.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
PublisherSociety for Industrial and Applied Mathematics Publications
Pages577-585
Number of pages9
ISBN (Print)9781510811522
StatePublished - 2015
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Other

OtherSIAM International Conference on Data Mining 2015, SDM 2015
CountryCanada
CityVancouver
Period4/30/155/2/15

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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