Advertisement

Chinese Micro-Blog Emotion Classification by Exploiting Linguistic Features and SVM\(^{\textit{perf}}\)

  • Hua XuEmail author
  • Fan Zhang
  • Jiushuo Wang
  • Weiwei Yang
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 639)

Abstract

These years, micro-blog emotion mining becomes one of the research hotspots in social network data mining. Different from state of the art study, this paper presents a novel method for emotion classification , which is SVM \(^{\textit{perf}}\) based method combined with syntactic structure of Chinese micro-blogs. The classified emotion type includes Happiness, Anger, Disgust, Fear, Sadness and Surprise. For the proposed method, an emotional lexicon is constructed and linguistic features are extracted from micro-blog corpus firstly. Secondly, for the current feature space dimension is higher, Chi-square test is used to extract the high-frequency and high-class relevance keywords. At the same time, Pointwise Mutual Information (PMI) is used to pick the effective low frequency words in feature dimension reduction, which can reduce the computational complexity. Finally, SVM\(^{\textit{perf}}\) is applied for the emotion classification. In order to illustrate the effectiveness of the algorithm, LIBSVM and SVM-Light are used as the baseline. The data from Sina Micro-blog (weibo.com) have been used as the experiment data. The experiment results demonstrate that all the above features contribute to emotion classification in micro-blogs, and the results validate the feasibility of the proposed approach. It also shows that SVM \(^{\textit{perf}}\) is an appropriate choice of classifier for emotion classification.

Keywords

Micro-blog Text mining Emotion classification SVM\(^{\textit{perf}}\) 

Notes

Acknowledgments

Supported by National Basic Research Program of China (973 Program) (Grant No:2012CB316301), National Natural Science Foundation of China (Grant No: 61175110), and Chinese National Programs for High Technology Research and Development (863 Program) (Grant No. 2013AA013702).

References

  1. 1.
    Su, Z., Zhou, B., Li, A., Han, Y.: Analysis on chinese microblog sentiment based on syntax parsing and support vector machine. Web Technol. Appl. 8710, 104–114 (2014)Google Scholar
  2. 2.
    Wen, S., Wan, X.: Emotion classification in microblog texts using class sequential rules. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 187–193. Association for the Advancement of Artificial Intelligence (2014)Google Scholar
  3. 3.
    Wei, W., Gulla, J.A.: Sentiment analysis in a hybrid hierarchical classification process. In: Proceedings of 7th International Conference on Digital Information Management, pp. 47–55. IEEE (2012)Google Scholar
  4. 4.
    Yang, D.H., Yu, G.: A method of feature selection and sentiment similarity for chinese micro-blogs. J. Inform. Sci. 39(1), 429–441 (2013)CrossRefGoogle Scholar
  5. 5.
    Yuan, Z., Purver, M.: Predicting emotion labels for chinese microblog texts. In: Proceedings of the First International Workshop on Sentiment Discovery from Affective Data (SDAD 2012), pp. 40–47. CEUR (2012)Google Scholar
  6. 6.
    Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124–129 (1971)CrossRefGoogle Scholar
  7. 7.
    Li, W., Xu, H.: Text-based emotion classification using emotion cause extraction. Expert Syst. Appl. 41(4), 1742–1749 (2014)CrossRefGoogle Scholar
  8. 8.
    Liu, Q., Feng, C., Huang, H.: Emotional tendency identification for micro-blog topics based on multiple characteristics. In: Proceedings of the 26th Pacific Asia Conference on Language, Information and Computation, pp. 280–288. Universitas Indonesia, Faculty of Computer Science (2012)Google Scholar
  9. 9.
    Pei, S., Zhang, L., Li, A.: Microblog sentiment analysis model based on emoticons. Web Technol. Appl. 8710, 127–135 (2014)Google Scholar
  10. 10.
    Bai, X., Chen, F., Zhan, S.: A study on sentiment computing and classification of sina weibo with word2vec. In: Proceedings of 2014 IEEE International Congress on Big Data (BigData Congress), pp. 358–363. IEEE (2014)Google Scholar
  11. 11.
    Desmet, B., Hoste, V.: Emotion detection in suicide notes. Expert Syst. Appl. 40(16), 6351–6358 (2013)CrossRefGoogle Scholar
  12. 12.
    Ghazi, D., Inkpen, D., Szpakowicz, S.: Prior and contextual emotion of words in sentential context. Comput. Speech Lang. 28, 76–92 (2014)CrossRefGoogle Scholar
  13. 13.
    Kim, M., Kwon, H.C.: Lyrics-based emotion classification using feature selection by partial syntactic analysis. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 960–964. IEEE (2011)Google Scholar
  14. 14.
    Agrawal, A., An, A.: Unsupervised emotion detection from text using semantic and syntactic relations. In: Proceedings of 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 346–353. IEEE (2012)Google Scholar
  15. 15.
    Ho, D.T., Cao, T.H.: A high-order hidden markov model for emotion detection from textual data. Knowl. Manage. Acquis. Intell. Syst. 7457, 94–105 (2012)CrossRefGoogle Scholar
  16. 16.
    Li, Y., Li, X., Li, F., Zhang, X.: A lexicon-based multi-class semantic orientation analysis for microblogs. Web Technol. Appl. 8709, 81–92 (2014)Google Scholar
  17. 17.
    Liu, S.M., Chen, J.H.: A multi-label classification based approach for sentiment classification. Expert Syst. Appl. 42(3), 1083–1093 (2015)CrossRefGoogle Scholar
  18. 18.
    Huang, S., Peng, W., Li, J., Lee, D.: Sentiment and topic analysis on social media: a multi-task multi-label classification approach. In: Proceedings of the 5th Annual ACM Web Science Conference, pp. 172–181. ACM (2013)Google Scholar
  19. 19.
    Li, T., Xiao, X., Xue, Q.: An unsupervised approach for sentiment classification. In: Proceedings of 2012 IEEE Symposium on Robotics and Applications, pp. 638–640. IEEE (2012)Google Scholar
  20. 20.
    He, H.: Sentiment analysis of sina weibo based on semantic sentiment space model. In: Proceedings of management science and engineering, pp. 206–211. IEEE (2013)Google Scholar
  21. 21.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)Google Scholar
  22. 22.
    Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods—Support Vector Learning, chap. 11, pp. 169–184. MIT Press, Cambridge, MA (1999)Google Scholar
  23. 23.
    Jiang, F., Cui, A., Liu, Y., Zhang, M., Ma, S.: Every term has sentiment: learning from emoticon evidences for chinese microblog sentiment analysis. In: Natural Language Processing and Chinese Computing, vol. 400, pp. 224–235 (2013)Google Scholar
  24. 24.
    Xu, L., Liu, H., Pan, Y., Ren, H., Chen, J.: Constructing the affective lexicon ontology. J. China Soc. Sci. Tech. Inf. 27(2), 180–185 (2008)Google Scholar
  25. 25.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of the 1st International Conference on Learning Representations (2013)Google Scholar
  26. 26.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)CrossRefGoogle Scholar
  27. 27.
    Zhang, P., He, Z.: A weakly supervised approach to chinese sentiment classification using partitioned self-training. J. Inform. Sci. 39(6), 815–831 (2013)CrossRefGoogle Scholar
  28. 28.
    Joachims, T.: A support vector method for multivariate performance measures. In: Proceedings of the 22th International Conference on Machine Learning (ICML), pp. 377–384. ACM (2005)Google Scholar
  29. 29.
    Joachims, T.: Training linear svms in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–226. ACM (2006)Google Scholar
  30. 30.
    Joachims, T., Yu, C.N.J.: Sparse kernel svms via cutting-plane training. Mach. Learn. 76(2–3), 179–193 (2009)CrossRefGoogle Scholar
  31. 31.
    Gao, K., Zhou, E.L., Grover, S.: Applied methods and techniques for modeling and control on micro-blog data crawler. In: Applied Methods and Techniques for Mechatronic Systems, vol. 452, pp. 171–188 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  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