References

Agarwal, A., B. Xie, I. Vovsha, O. Rambow and R. Passonneau, 2011. Sentiment analysis of twitter data. Proceedings of the Workshop on Languages in Social Media, June 23, 2011, Association for Computational Linguistics Stroudsburg, PA, USA, pp: 30-38.

Agarwal, B., S. Poria, N. Mittal, A. Gelbukh and A. Hussain, 2015. Concept-level sentiment analysis with dependency-based semantic parsing: A novel approach. Cognit. Comput., 7: 487-499.
CrossRef  |  Direct Link  |  

Anjaria, M. and R.M.R. Guddeti, 2014. A novel sentiment analysis of social networks using supervised learning. Soc. Netw. Anal. Min., 4: 181-195.
CrossRef  |  Direct Link  |  

Appel, O., F. Chiclana, J. Carter and H. Fujita, 2016. A hybrid approach to the sentiment analysis problem at the sentence level. Knowl. Based Syst., 108: 110-124.
Direct Link  |  

Asmi, A. and T. Ishaya, 2012. Negation identification and calculation in sentiment analysis. Proceedings of the 2nd International Conference on Advances in Information Mining and Management (IMMM), October 21-26, 2012, IARIA, Wilmington, Delaware, ISBN:978-1-61208-227-1, pp: 1-7.

Bhadane, C., H. Dalal and H. Doshi, 2015. Sentiment analysis: Measuring opinions. Procedia Comput. Sci., 45: 808-814.
CrossRef  |  Direct Link  |  

Chikersal, P., S. Poria and E. Cambria, 2015. SeNTU: Sentiment analysis of tweets by combining a rule-based classifier with supervised learning. Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval’15), June 4-5, 2015, Association for Computational Linguistics, Denver, Colorado, pp: 647-651.

Hu, M. and B. Liu, 2004. Mining and summarizing customer reviews. Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 22-25, 2004, ACM, Seattle, Washington, USA., ISBN:1-58113-888-1, pp: 168-177.

Hutto, C.J. and E. Gilbert, 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th AAAI International Conference on Weblogs and Social Media (ICWSM’14), June 1-4, 2014, Association for the Advancement of Artificial Intelligence, Palo Alto, California‎, USA., pp: 216-225.

Jianqiang, Z. and G. Xiaolin, 2017. Comparison research on text pre-processing methods on twitter sentiment analysis. IEEE Access, 5: 2870-2879.
CrossRef  |  Direct Link  |  

Kawathekar, S.A. and M.M. Kshirsagar, 2012. Sentiments analysis using hybrid approach involving rule-based and support vector machines methods. IOSRJEN., 2: 55-58.

Khan, F.H., S. Bashir and U. Qamar, 2014. TOM: Twitter opinion mining framework using hybrid classification scheme. Decis. Support Syst., 57: 245-257.
Direct Link  |  

Khan, F.H., U. Qamar and S. Bashir, 2017. A semi-supervised approach to sentiment analysis using revised sentiment strength based on SentiWordNet. Knowl. Inf. Syst., 51: 851-872.
CrossRef  |  Direct Link  |  

Khan, J. and B.S. Jeong, 2016. Summarizing customer review based on product feature and opinion. Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC’16) Vol. 1, July 10-13, 2016, IEEE, Jeju, South Korea, ISBN:978-1-5090-0391-4, pp: 158-165.

Kouloumpis, E., T. Wilson and J.D. Moore, 2011. Twitter sentiment analysis: The good the bad and the OMG!. Proceedings of the AAAI 5th International Conference on Weblogs and Social Media (ICWSM), July 17-21, 2011, AAAI, Barcelona, Spain, pp: 538-541.

Medhat, W., A. Hassan and H. Korashy, 2014. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J., 5: 1093-1113.
CrossRef  |  Direct Link  |  

Muhammad, A., N. Wiratunga and R. Lothian, 2016. Contextual sentiment analysis for social media genres. Knowl. Based Syst., 108: 92-101.
CrossRef  |  Direct Link  |  

Mukwazvure, A. and K.P. Supreethi, 2015. A hybrid approach to sentiment analysis of news comments. Proceedings of the 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), September 2-4, 2015, IEEE, Noida, India, ISBN:978-1-4673-7230-5, pp: 1-6.

Narr, S., M. Hulfenhaus and S. Albayrak, 2012. Language-independent Twitter sentiment analysis. Proceedings of the 2012 Joint Workshop on Knowledge Discovery and Machine Learning (KDML’12) and Lernen Wissen Adaption, September 12-14, 2012, Technical University of Dortmund, Dortmund, Germany, pp: 1-7.

Neviarouskaya, A., H. Prendinger and M. Ishizuka, 2009. Semantically distinct verb classes involved in sentiment analysis. Proceedings of the IADIS International Conference on Applied Computing (ICAC’09), November 19-21, 2009, International Association for Development of the Information Society (IADIS), Portugal, Lisbon, ISBN:978-972-8924-97-3, pp: 27-35.

Pak, A. and P. Paroubek, 2010. Twitter as a corpus for sentiment analysis and opinion mining. Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC’10), May 17-23, 2010, European Language Resources Association, Valletta, Malta, ISBN:2-9517408-6-7, pp: 1320-1326.

Prabowo, R. and M. Thelwall, 2009. Sentiment analysis: A combined approach. J. Inform., 3: 143-157.
CrossRef  |  Direct Link  |  

Revathy, K. and B. Sathiyabhama, 2013. A hybrid approach for supervised twitter sentiment classification. Intl. J. Comput. Sci. Bus. Inf., 7: 35-45.
Direct Link  |  

Saif, H., Y. He and H. Alani, 2012. Semantic sentiment analysis of Twitter. Proceedings of the 11th International Conference on Semantic Web, November 11-15, 2012, Springer, Boston, Massachusetts, USA., ISBN:978-3-642-35175-4, pp: 508-524.

Saif, H., Y. He, M. Fernandez and H. Alani, 2016. Contextual semantics for sentiment analysis of Twitter. Inf. Process. Manage., 52: 5-19.
Direct Link  |  

Silva, N.F.D., E.R. Hruschka and E.R. Hruschka, 2014. Tweet sentiment analysis with classifier ensembles. Decis. Support Syst., 66: 170-179.
Direct Link  |  

Spencer, J. and G. Uchyigit, 2012. Sentimentor: Sentiment analysis of Twitter data. Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD’12), September 28, 2012, University of Bristol, Bristol, England, UK., pp: 56-66.