1. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010), (Valletta, Malta), European Language Resources Association (ELRA), May 2010

   Google Scholar 

2. Go, A., Bhayani, R.: Twitter sentiment classificationusing distant supervision. CS224N Project Rep. Stanford1, 1–12 (2009)

   Google Scholar 

3. Niu, Z., Yin, Z., Kong, X.: Sentiment classification formicroblog by machine learning. In: 2012 Fourth International Conference on Computational and Information Sciences (ICCIS), pp. 286–289, August 2012

   Google Scholar 

4. Lek, H.H., Poo, D.: Aspect-based twitter sentimentclassification. In: 2013 IEEE 25th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 366–373, November 2013

   Google Scholar 

5. 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)

   Article  Google Scholar 

6. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM56, 82–89 (2013)

   Article  Google Scholar 

7. Zhang, Y., Dang, Y., Chen, H.: Research note: examining gender emotional differences in web forum communication. Decis. Support Syst.55(3), 851–860 (2013)

   Article  Google Scholar 

8. Bhuta, S., Doshi, A., Doshi, U., Narvekar, M.: A review oftechniques for sentiment analysis of twitter data. In: 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), pp. 583–591, February 2014

   Google Scholar 

9. 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, EMNLP 2002, vol. 10, pp. 79–86, Association for Computational Linguistics, Stroudsburg, PA, USA (2002)

   Google Scholar 

10. Brychcin, T., Konkol, M., Steinberger, J.: UWB: machine learning approach to aspect-based sentiment analysis, SemEval 2014, p. 817 (2014)

    Google Scholar 

11. Liu, K.L., Li, W.J., Guo, M.: Emoticon smoothed language modelsfor twitter sentiment analysis, vol. 2, pp. 1678–1684, 2012. cited By (since 1996)

    Google Scholar 

12. Kansal, H., Toshniwal, D.: Aspect based summarization of context dependent opinion words. Procedia Comput. Sci.35, 166–175 (2014). 2014 Proceedings of 18th Annual Conference on Knowledge-Based and Intelligent Information and amp; Engineering Systems, KES-2014 Gdynia, Poland, September

    Article  Google Scholar 

13. Zhang, W., Xu, H., Wan, W.: Weakness finder: find product weakness from chinese reviews by using aspects based sentiment analysis. Expert Syst. Appl.39(11), 10283–10291 (2012)

    Article  Google Scholar 

14. Jmal, J., Faiz, R.: Customer review summarization approach using twitter and sentiwordnet. In: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, WIMS 2013, pp. 33:1–33:8, New York, NY, USA. ACM (2013)

    Google Scholar 

15. Marrese-Taylor, E., Velsquez, J.D., Bravo-Marquez, F.: A novel deterministic approach for aspect-based opinion mining in tourism products reviews. Expert Syst. Appl.41(17), 7764–7775 (2014)

    Article  Google Scholar 

16. Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Calzolari, N., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M., Tapias, D. (eds.) LREC, European Language Resources Association (2010)

    Google Scholar 

17. Zainuddin, N., Selamat, A., Ibrahim, R.: Improving twitter aspect-based sentiment analysis using hybrid approach. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.P. (eds.) ACIIDS 2016, Part I. LNCS, vol. 9621, pp. 151–160. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

18. Liu, B.: Sentiment Analysis and Subjectivity: Handbook of Natural Language Processing, 2nd edn. CRC Press, Boca Raton (2010)

    Google Scholar 

19. Esuli, A., Sebastiani, F.: Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of LREC, vol. 6, pp. 417–422 (2006)

    Google Scholar 

20. Selamat, A., Omatu, S.: Web page feature selection and classification using neural networks. Inf. Sci.158, 69–88 (2004)

    Article MathSciNet  Google Scholar 

21. Vinodhini, G., Chandrasekaran, M.R.: Opinion mining using principal component analysis based ensemble model for e-commerce application. CSI Trans. ICT2(3), 169–179 (2014)

    Article  Google Scholar 

22. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn.20(3), 273–297 (1995)

    MATH  Google Scholar 

23. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

24. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, LSM 2011, pp. 30–38, Association for Computational Linguistics, Stroudsburg, PA, USA (2011)

    Google Scholar 

Download references