Advertisement

Sentiment Word Identification with Sentiment Contextual Factors

  • Jiguang LiangEmail author
  • Xiaofei Zhou
  • Yue Hu
  • Li Guo
  • Shuo Bai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9313)

Abstract

Sentiment word identification (SWI) refers to the task of automatically identifying whether a given word expresses positive or negative opinion. SWI is a critical component of sentiment analysis technologies. Traditional sentiment word identification techniques become unqualified because they need seed sentiment words which leads to low robustness. In this paper, we consider SWI as a matrix factorization problem and propose three models for it. Instead of seed words, we exploit sentiment matching and sentiment consistency for modeling. Extensive experimental studies on three real-world datasets demonstrate that our models outperform the state-of-the-art approaches.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bross, J., Ehrig, H.: Automatic construction of domain and aspect specific sentiment lexicons for customer review mining. In: CIKM, pp. 1077–1086 (2013)Google Scholar
  2. 2.
    Ding, X., Liu, B., Yu, P.: A holistic lexicon-based approach to opinion mining. In: WSDM, pp. 231–240 (2008)Google Scholar
  3. 3.
    Ganesan, K., Zhai, C.X., Viegas, E.: Micropinion generation: an unsupervised approach to generating ultra-concise summaries of opinions. In: WWW, pp. 869–878 (2012)Google Scholar
  4. 4.
    Golub, G., Reinsch, C.E.: Singular value decomposition and least squares solutions. Numerische Mathematik 14(5), 403–420 (1970)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: ACL, pp. 174–181 (1997)Google Scholar
  6. 6.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: 10th Proceeding of ACM SIGKDD, pp. 168–177 (2004)Google Scholar
  7. 7.
    Kamps, J., Marx, M., Mokken, R.J., De Rijke, M.: Using WordNet to measure semantic orientation of adjectives. In: LREC, pp. 1115–1118 (2004)Google Scholar
  8. 8.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  9. 9.
    Liang, J.G., Zhou, X.F., Hu, Y., Guo, L., Bai, S.: CONR: A novel method for sentiment word identification. In: CIKM, pp. 1943–1946 (2014)Google Scholar
  10. 10.
    Lu, Y., Castellanos, M., Dayal, U., Zhai, C.X.: Automatic construction of a context-aware sentiment lexicon: An optimization approach. In: WWW, pp. 347–356 (2011)Google Scholar
  11. 11.
    Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: ACL, pp. 142–150 (2011)Google Scholar
  12. 12.
    Miller, G.A.: WordNet: A lexical database for English. Communication of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  13. 13.
    Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278 (2004)Google Scholar
  14. 14.
    Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: EMNLP, pp. 339–346 (2005)Google Scholar
  15. 15.
    Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: IJCAI, pp. 1199–1204 (2009)Google Scholar
  16. 16.
    Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: EACL, pp. 675–682 (2009)Google Scholar
  17. 17.
    Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21(4), 315–346 (2003)CrossRefGoogle Scholar
  18. 18.
    Vechtomova, O., Suleman, K., Thomas, J.: An information retrieval-based approach to determining contextual opinion polarity of words. In: ECIR, pp. 507–512 (2014)Google Scholar
  19. 19.
    Yu, H., Deng, Z.H., Li, S.: Identifying sentiment words using an optimization-based model without seed words. In: ACL, pp. 855–859 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jiguang Liang
    • 1
    Email author
  • Xiaofei Zhou
    • 1
  • Yue Hu
    • 1
  • Li Guo
    • 1
  • Shuo Bai
    • 1
  1. 1.National Engineering Laboratory for Information Security TechnologiesInstitute of Information Engineering, Chinese Academy of SciencesBeijingChina

Personalised recommendations