Cluster Computing

, Volume 21, Issue 1, pp 985–995 | Cite as

Sentiment analysis of short texts in microblog based on ependency parsing

  • Lirong QiuEmail author
  • Jie Li


Traditional approaches to analyzing short text sentiment rarely consider the relationship between emotional words and modifiers. Most traditional methods simply accumulate the sentiment of the sentence to obtain the sentiment of short text. In this paper, we propose a method to mitigate the problems through sentiment structure and sentiment calculation rules. The sentiment structure is obtained from the dependency parsing process with the relationship migration and modified distance, which makes a solid contribution to analyzing the sentiment of short text. The sentiment of short text is accumulated according to the different influence of relationships between the clauses and the contribution of each sentence to the sentiment calculation of short text. Experiment result indicates the effective of the approach.


Sentiment analysis Sentiment structure Sentiment polarity Short text 



This work was supported by the National Nature Science Foundation of China (No. 61672553) and the (Ministry of Education in China) Project of Humanities and Social Sciences (Project No. 16YJCZH076). All above funds did not lead to any conflict of interests regarding the publication of this manuscript.


  1. 1.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 168–177 (2004)Google Scholar
  2. 2.
    Zhao, Y.Y., Qin, B., Liu, T.: Sentiment analysis. J. Softw. 21(8), 1834–1848 (2010)CrossRefGoogle Scholar
  3. 3.
    Yang, A., Lin, J., Zhou, Y.: Method on building Chinese text sentiment Lexicon. J. Front. Comput. Sci. Technol. 7(11), 1033–1039 (2013)Google Scholar
  4. 4.
    Xu, G., Meng, X., Wang, H.: Build Chinese emotion lexicons using a graph-based algorithm and multiple resources. In: International conference on computational linguistics, proceedings of the conference, 23–27 August 2010, Beijing, China, pp. 1209–1217 (2010)Google Scholar
  5. 5.
    Jiang, D., Luo, X., Xuan, J., Xu, Z.: Sentiment computing for the news event based on the social media big data. IEEE Access 5, 2373–2382 (2017)CrossRefGoogle Scholar
  6. 6.
    Wang, X., Zhang, H., Xu, Z.: Public Sentiments Analysis Based on Fuzzy Logic for Text. International Journal of Software Engineering and Knowledge Engineering 26(9–10), 1341–1360 (2016)CrossRefGoogle Scholar
  7. 7.
    Taboada, M., Brooke, J., Tofiloski, M., et al.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  8. 8.
    Xia, R., Wang, C., Dai, X.Y., et al.: Co-training for Semi-supervised sentiment classification based on dual-view bags-of-words representation. ACL (1), 1054–1063 (2015)Google Scholar
  9. 9.
    Tang, D., Wei, F., Qin, B., et al.: Building large-scale Twitter-specific sentiment Lexicon: a representation learning approach. COLING, 172–182 (2014)Google Scholar
  10. 10.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, Vol. 10. Association for Computational Linguistics, pp. 79–86 (2002)Google Scholar
  11. 11.
    Cui, H., Mittal, V., Datar, M.: Comparative experiments on sentiment classification for online product reviews. AAAI 6, 1265–1270 (2006)Google Scholar
  12. 12.
    Barbosa, L., Feng, J.: Robust sentiment detection on Twitter from biased and noisy data. In: Proceedings of COLING 2010, Beijing, China, pp. 36–44 (2010)Google Scholar
  13. 13.
    Zhao, J., Liu, K., Wang, G.: Adding redundant features for CRFs-based sentence sentiment classification. In: Proceedings of the conference on empirical methods in natural language processing. Association for Computational Linguistics, pp. 117–126 (2008)Google Scholar
  14. 14.
    Agarwal, A., Xie, B., Vovsha, I., et al.: Sentiment analysis of Twitter data. In: Proceedings of the workshop on language in social media (LSM 2011), Portland, Oregon, pp. 30–38 (2011)Google Scholar
  15. 15.
    Zhang, C.G., Liu, P.Y., Zhu, Z.F., et al.: A sentiment analysis method based on a polarity lexicon. J. Shandong Univ. 47(3), 47–50 (2012)Google Scholar
  16. 16.
    Shi, F., Fu, Y., Feng, Y., et al.: Blog sentiment orientation analysis based on dependency parsing. J. Comput. Res. Dev. 49(11), 2395–2406 (2012)Google Scholar
  17. 17.
    Zhang, S., Yu, L., Hu, C.: Sentiment analysis of Chinese micro-blog based on emotions and emotional words. Comput. Sci. 39(11A), 146–176 (2012)Google Scholar
  18. 18.
    Xie, L., Zhou, M., Sun, M.: Hierarchical structure based hybrid approach to sentiment analysis of Chinese micro blog and its feature extraction. J. Chin. Inf. Process. 26(1), 73–83 (2012)Google Scholar
  19. 19.
    Yao, T.F., Nie, Q.Y., Li, J.C., et al.: An opinion mining system for Chinese automobile reviews. Front. Chin. Inf. Process., 260–281 (2006)Google Scholar
  20. 20.
    Yang, L., Geng, X., Liao, H.: A web sentiment analysis method on fuzzy clustering for mobile social media users. Eurasip J. Wirel. Commun. Netw. 2016(1), 1–13 (2016)CrossRefGoogle Scholar
  21. 21.
    Li, A., Di, P., Duan, L.: Document sentiment orientation analysis based on sentence weighted algorithm. J. Chin. Comput. Syst. 36(10), 2252–2256 (2015)Google Scholar
  22. 22.
    Li, S., Zhang, H., Xu, W., et al. Chinese text sentiment analysis based on combination model. In: China Conference on Information Retrieval (2009)Google Scholar
  23. 23.
    Liang, J., Chai, Y., Yuan, H., et al.: Deep learning for Chinese micro-blog sentiment analysis. J. Chin. Inf. Process. 28(5), 155–161 (2014)Google Scholar
  24. 24.
    Sun, J., Xueqiang, L., Zhang, L.: On sentiment analysis of Chinese microblogging based on lexicon and machine learning. Comput. Appl. Softw. 7, 177–181 (2014)Google Scholar
  25. 25.
    Cheng, N.C., Hou, M., Teng, Y.L.: Short text attitude analysis based on textual characteristics. J. Chin. Inf. Process. 29(3), 163–169 (2015)Google Scholar
  26. 26.
    Li, A., Di, P., Duan, L.: Document sentiment orientation analysis based on sentence weighted algorithm. J. Chin. Comput. Syst. 36(10), 2252–2256 (2015)Google Scholar
  27. 27.
    Kai, G.A.O., Siyu, L.I., Dongru, R.U.A.N., et al.: A micro-blog sentiment analysis approach. J. Chin. Inf. Process. 29(4), 40–49 (2015)Google Scholar
  28. 28.
    Zhang, J., Sun, Y., Wang, H., et al.: Calculating statistical similarity between sentences. J. Converg. Inf. Technol, 6(2) (2011)Google Scholar

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© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Information EngineeringMinzu University of ChinaBeijingChina

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