Cluster Computing

, Volume 22, Supplement 5, pp 12619–12632 | Cite as

Sentiment classification and computing for online reviews by a hybrid SVM and LSA based approach

  • Wei Zhang
  • Sui-xi Kong
  • Yan-chun ZhuEmail author
  • Xiao-le Wang


For the current online reviews sentiment classification method, there are some problems such as serious text sparseness and coarse granularity of sentiment calculation. In this paper, the emotion in online reviews is divided into four categories: happiness, hope, disgust, and anxiety. Based on the combination of cognitive evaluation theory and sentiment analysis, a novel approach that combines a well-known techniques to sentiment classification, ie, support vector machine and the latent semantic analysis, was proposed. Based on the approach, this paper explored the influence of these four kinds of emotions on the helpfulness of online reviews, examined the moderating effects of emotion on the helpfulness of online reviews under the two types of products. The experimental results showed that this model could effectively conduct multi-emotion fine-grained computing for online reviews, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that happiness and disgust emotion had significant positive impact on the helpfulness of online reviews, while on the other hand anxiety emotion had significant negative influence. The algorithm and its empirical conclusions provide useful theoretical basis and reference for the company to optimize marketing strategy and improve customer relationship under web 2.0.


Online reviews Sentiment analysis Latent semantic analysis Support vector machine 



This work was supported by Key Technologies Research and Development Program of China (2017YFB1400103), Beijing Municipal Natural Science Foundation (9182016) and MOE (Ministry of Education in China) Project of Humanities and Social Sciences (17YJAZH120). We wish to thank the anonymous reviewers who helped to improve the quality of the paper. The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.


  1. 1.
    Lee, E.J., Shin, S.Y.: When do consumers buy online product reviews? Effects of review quality, product type, and reviewer’s photo. Comput. Hum. Behav. 31, 356–366 (2014)CrossRefGoogle Scholar
  2. 2.
    Chen, Y.B., Xie, J.H.: Online consumer review: word-of-mouth as a new element of marketing communication mix. Manag. Sci. 54(3), 477–491 (2008)CrossRefGoogle Scholar
  3. 3.
    Duan, W.J., Gu, B., Whinston, A.B.: Do online reviews matter? An empirical investigation of panel data. Decis. Support Syst. 45(4), 1007–1016 (2008)CrossRefGoogle Scholar
  4. 4.
    Niu, G., Li, G., Geng, X.: The impact of online reviews’ quality and quantity on online purchasing intention: the moderating effect of need for cognition. J. Psychol. Sci. 39(6), 1454–1459 (2016)Google Scholar
  5. 5.
    Wu, C., Che, H., Chan, T.Y.: The economic value of online reviews. Mark. Sci. 34(5), 739–754 (2015)CrossRefGoogle Scholar
  6. 6.
    Liu, T., Zhang, C., Wu, M.: An algorithm of online product feature extraction based on boundary average entropy. Syst. Eng. 36(9), 2416–2423 (2016)Google Scholar
  7. 7.
    Liu, B.: Sentiment Analysis: Mining Sentiments, Opinions, and Emotions. Cambridge University Press, Cambridge (2015)Google Scholar
  8. 8.
    Ghiassi, M., Skinner, J., Zimbra, D.: Twitter brand sentiment analysis: a hybrid system using n -gram analysis and dynamic artificial neural network. Expert Syst. Appl. 40(16), 6266–6282 (2013)CrossRefGoogle Scholar
  9. 9.
    Pang, B., Lee. L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of ACL-02 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Philadelphia, PA, USA (2002)Google Scholar
  10. 10.
    Ye, Q., Zhang, Z., Law, R.: Sentiment classification of online reviews to travel destinations by supervised machine learning approaches. Expert Syst. Appl. 36(3), 6527–6535 (2009)CrossRefGoogle Scholar
  11. 11.
    Narendra, B., Sai, K.U., Rajesh, G.: Sentiment analysis on movie reviews: a comparative study of machine learning algorithms and open source technologies. Int. J. Intell. Syst. Technol. Appl. 8(8), 66–70 (2016)Google Scholar
  12. 12.
    Yang, D., Yang, A.M.: Classification approach of Chinese texts sentiment based on semantic lexicon and naive Bayesian. Appl. Res. Comput. 27(10), 3737–3739 (2010)Google Scholar
  13. 13.
    Pang, Bo, Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1), 1–135 (2008)CrossRefGoogle Scholar
  14. 14.
    Frijda, N.H., Kuipers, P., Terschure, E.: Relations among emotion, appraisal, and emotional action readiness. J. Pers. Soc. Psychol. 57(2), 212–228 (1989)CrossRefGoogle Scholar
  15. 15.
    Yin, D.Z., Samuel, D.B., Zhang, H.: Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. Mis Q. 38(2), 539–560 (2014)CrossRefGoogle Scholar
  16. 16.
    Liu, L.N., Qi, J.Y., Qi, H.W., Jiang, S.: Exploring the distribution of discrete emotions in online reviews. Inf. Sci. 8, 121–128 (2017)Google Scholar
  17. 17.
    Sun, X., Peng, X.Q., Hu, M., Ren, F.J.: Extended multi-modality features and deep learning based microblog short text sentiment analysis. J. Electron. Inf. Technol. 39(9), 2048–2055 (2017)Google Scholar
  18. 18.
    Zhao, Y.Y., Qin, B., Liu, T.: Sentiment analysis. J. Softw. 21(8), 1834–1848 (2010)CrossRefGoogle Scholar
  19. 19.
    Zhang, Z.Q., Qiang, Y., Li, Y.J.: Literature review on sentiment analysis of online product reviews. J. Manag. Sci. China 13(6), 84–96 (2010)Google Scholar
  20. 20.
    Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl. Syst. 89, 14–46 (2015)CrossRefGoogle Scholar
  21. 21.
    Rana, T.A., Cheah, Y.N.: Aspect extraction in sentiment analysis: comparative analysis and survey. Artif. Intell. Rev. 1–25 (2016)Google Scholar
  22. 22.
    Zhang, L., Li, S., Peng, J., Chen, L., Li, H.Y.: Feature-opinion pairs classification based on dependency relations and maximum entropy model. J. Univ. Electron. Sci. Technol. China 43(3), 420–425 (2014)zbMATHGoogle Scholar
  23. 23.
    Yin, P., Wang, H.W.: Sentiment classification for chinese online reviews at product feature level through domain ontology method. J. Syst. Manag. 25(1), 103–114 (2016)Google Scholar
  24. 24.
    Jiang, S.Y., Huang, W.J., Cai, M.L., Wang, L.X.: Building social emotional lexicons for emotional analysis on microblog. J. Chin. Inf. Process. 29(6), 166–171 (2015)Google Scholar
  25. 25.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. Proc. Hum. Lang. Technol. Conf. Empir. Methods Nat. Lang. Process. 7, 347–354 (2005)Google Scholar
  26. 26.
    Andreevskaia, A., Bergler, S.: Mining WordNet for a fuzzy sentiment: sentiment tag extraction from WordNet glosses. In: Proceedings of the Conference on the European Chapter of the Association for Computational Linguistics, April 3–7, 2006, Trento, Italy(2006)Google Scholar
  27. 27.
    Wan, C.X., Jiang, T.J., Zhong, M.J., Bian, H.R.: Sentiment computing of web financial information based on the part-of-speech tagging and dependency parsing. J. Comput. Res. Dev. 50(12), 2554–2569 (2013)Google Scholar
  28. 28.
    Montejo-Ráez, A., Martínez-Cámara, E., Martín-Valdivia, M.T.: Ranked Wordnet graph for Sentiment Polarity Classification in Twitter. Comput. Speech Lang. 28(1), 93–107 (2014)CrossRefGoogle Scholar
  29. 29.
    Huang, F.L., Feng, S., Wang, D.L., YU, G.: Mining topic sentiment in microblogging based on multi-feature fusion. Chin. J. Comput. 40(4), 872–888 (2017)Google Scholar
  30. 30.
    Li, X., Li, J., Wu, Y.A.: Global optimization approach to multi-polarity sentiment analysis. Plos ONE 10(4), e0124672(2015)Google Scholar
  31. 31.
    Ma, B.Z., Yan, Z.J.: Product features extraction of online reviews based on lda model. Comput. Integr. Manuf. Syst. 20(1), 96–103 (2014)Google Scholar
  32. 32.
    Xiong, S.F., Ji, D.H.A.: Short text sentiment-topic model for product review analysis. Acta Autom. Sin. 42(8), 1227–1237 (2016)Google Scholar
  33. 33.
    Xu, L., Lin, H., Zhao, J.: Construction and analysis of emotional corpus. J. Chin. Inf. Process. 22(1), 116–122 (2008)Google Scholar
  34. 34.
    Wilson, T.A.: Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states. University of Pittsburgh, Pittsburgh, PA, USA (2008)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wei Zhang
    • 1
  • Sui-xi Kong
    • 1
  • Yan-chun Zhu
    • 2
    Email author
  • Xiao-le Wang
    • 3
  1. 1.School of InformationCentral University of Finance and EconomicsBeijingChina
  2. 2.Business SchoolBeijing Normal UniversityBeijingChina
  3. 3.School of Culture and CommunicationCentral University of Finance and EconomicsBeijingChina

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