Advertisement

Dynamic Forest Model for Sentiment Classification

  • Mingming Li
  • Jiao DaiEmail author
  • Wei Liu
  • Jizhong Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10638)

Abstract

Sentiment classification is a useful approach to analyse the emotional polarity of user reviews, and method based on machine learning has achieved a great success. In the era of Web2.0, the emotional intensity of terms will change with time and events, while a large number of Out-Of-Vocabulary (OOV) terms are appearing. But the method of machine learning pays little attention to them because they focus to reduce the computational complexity. To address the problem, we proposed a dynamic forest model, which can describe the emotional intensity of the term in character granularity, and can append OOV dynamically and adjust their emotional intensity value. Experiments show that in the Chinese environment, our model greatly boosts the performance compared with the method based machine learning, while the time is saved by halves.

Keywords

Sentiment classification Machine learning Out-Of-Vocabulary Sentiment lexicon Dynamic forest 

Notes

Acknowledgments

We would like to thank Sougou for its news data.

References

  1. 1.
    Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. (TOIS) 26(3), 12 (2008)CrossRefGoogle Scholar
  2. 2.
    Pang, B., Lee, L., et al.: Opinion mining and sentiment analysis. Found. Trends\(\textregistered \) Inf. Retrieval 2(1–2), 1–135 (2008)Google Scholar
  3. 3.
    Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C., Zhai, C. (eds.) Mining Text Data, pp. 41–463. Springer, Heidelberg (2012). doi: 10.1007/978-1-4614-3223-4_13
  4. 4.
    Vinodhini, G., Chandrasekaran, R.: Sentiment analysis and opinion mining: a survey. Int. J. 2(6), 282–292 (2012)Google Scholar
  5. 5.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  6. 6.
    Yong-Mei, Z., Yang Jia-Neng, Y.A.M.: A method on building Chinese sentiment Lexicon for text sentiment analysis. J. ShanDong Univ. (Eng. Sci.) 43(6), 27–33 (2013)Google Scholar
  7. 7.
    Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the OMG! Icwsm 11(538–541), 164 (2011)Google Scholar
  8. 8.
    Paltoglou, G., Thelwall, M.: A study of information retrieval weighting schemes for sentiment analysis. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1386–1395. Association for Computational Linguistics (2010)Google Scholar
  9. 9.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREc, vol. 10 (2010)Google Scholar
  10. 10.
    Vo, D.T., Zhang, Y.: Target-dependent Twitter sentiment classification with rich automatic features. In: IJCAI, pp. 1347–1353 (2015)Google Scholar
  11. 11.
    Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in Twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 841–842. ACM (2010)Google Scholar
  12. 12.
    Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using Wikipedia. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 787–788. ACM (2007)Google Scholar
  13. 13.
    Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for Twitter sentiment classification. In: ACL, vol. 1, pp. 1555–1565 (2014)Google Scholar
  14. 14.
    Shoushan, L., ChuRan, H.: Chinese sentiment classification based on stacking combination method. J. Chin. Inf. Process. 24(5), 56–61 (2010)Google Scholar
  15. 15.
    Yang, D., Yang, A.M.: Classification approach of Chinese texts sentiment based on semantic Lexicon and naive Bayesian. Jisuanji Yingyong Yanjiu 27(10) (2010)Google Scholar
  16. 16.
    Jie, J., Rui, X., et al.: Microblog sentiment classification via combining rule-based and machine learning methods. Acta Scientiarum Naturalium Universitatis Pekinensis 53(2), 247–254 (2017)MathSciNetGoogle Scholar
  17. 17.
    Li, L., Cao, D., Li, S., Ji, R.: Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 4798–4802. IEEE (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

Personalised recommendations