Lexicon-Based Sentiment Analysis on Topical Chinese Microblog Messages

  • Anqi Cui
  • Haochen Zhang
  • Yiqun Liu
  • Min Zhang
  • Shaoping Ma
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Microblogging is a popular social media where people express their opinions and sentiment on social topics. The Chinese microblogging service, called Weibo, has become a remarkable media in the Chinese society. People are eager to know others’ attitudes towards social events; thus sentiment analysis on those topical microblog messages is important. In this paper we introduce a lexicon-based sentiment analysis method. We construct a Weibo Lexicon with representative topical words and out-of-vocabulary (OOV) words, which are usually informal and are not existing in formal dictionaries. In addition, we use a propagation algorithm to automatically assign sentiment polarity scores to the discovered words. These scores are more closely reflecting the Weibo context since words may have new or opposite polarities instead of their formal meanings. Evaluations on the classification tasks show that our method is effective on recognizing the subjectivity and sentiment of Weibo sentences. The Weibo lexicon increases the performance of the classifications.

References

  1. 1.
    Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)Google Scholar
  2. 2.
    CNNIC: The 30th china internet development report. Tech. rep., China Internet Information Center (2012)Google Scholar
  3. 3.
    Cui, A., Zhang, M., Liu, Y., Ma, S.: Emotion tokens: bridging the gap among multilingual twitter sentiment analysis. In: Proceedings of the 7th Asia conference on Information Retrieval Technology, pp. 238–249. AIRS’11, Springer, Berlin, Heidelberg (2011)Google Scholar
  4. 4.
    Ku, L.W., Chen, H.H.: Mining opinions from the web: Beyond relevance retrieval. J. Am. Soc. Inf. Sci. Technol. 58(12), 1838–1850 (2007)CrossRefGoogle Scholar
  5. 5.
    Li, Z., Zhang, M., Ma, S., Zhou, B., Sun, Y.: Automatic extraction for product feature words from comments on the web. In: Proceedings of the 5th Asia Information Retrieval Symposium on Information Retrieval Technology, pp. 112–123. AIRS ’09, Springer, Berlin, Heidelberg (2009)Google Scholar
  6. 6.
    Liu, B.: Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2nd edn. In: Indurkhya, N., Damerau, FJ. (eds.) pp. 627–666 (2010)Google Scholar
  7. 7.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: SentiFul: A lexicon for sentiment analysis. IEEE Trans. Affect. Comput. 2(1), 22–36 (2011)CrossRefGoogle Scholar
  8. 8.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC, vol. 2010 (2010)Google Scholar
  9. 9.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations Trends Inform. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  10. 10.
    Zhang, H.P., Yu, H.K., Xiong, D.Y., Liu, Q.: Hhmm-based chinese lexical analyzer ictclas. In: Proceedings of the second SIGHAN workshop on Chinese language processing - vol. 17, pp. 184–187. SIGHAN ’03, Association for Computational Linguistics, Stroudsburg, PA (2003)Google Scholar
  11. 11.
    Zhang, W., Liu, J., Guo, X.: Positive and Negative Words Dictionary for Students (First Edition). Beijing, China: Encyclopedia of China Publishing House, 75–77 (2004)Google Scholar
  12. 12.
    Zhao, J., Dong, L., Wu, J., Xu, K.: Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1528–1531. ACM, New York (2012)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Anqi Cui
    • 1
  • Haochen Zhang
    • 1
  • Yiqun Liu
    • 1
  • Min Zhang
    • 1
  • Shaoping Ma
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

Personalised recommendations