Micro blogging websites like Twitter and Facebook, in this new era, is loaded with opinions and data. One of the most widely used micro-blogging site, Twitter, is where people share their ideas in the form of tweets and therefore it becomes one of the best sources for sentimental analysis. Opinions can be widely grouped into three categories good for positive, bad for negative and neutral and the process of analyzing differences of opinions and grouping them in all these categories is known as Sentiment Analysis. Data mining is basically used to uncover relevant information from web pages especially from the social networking sites. Merging data mining with other fields like text mining, NLP and computational intelligence we are able to classify tweets as good, bad or neutral. In order to improve classification results in the domain of sentiment analysis, we are using ensemble machine learning techniques for increasing the efficiency and reliability of proposed approach. For the same, we are using Linear Support Vector Machine and experimental results prove that our proposed approach is providing better classification results in terms of f-measure and accuracy in contrast to individual classifiers. We also use accuracy comparison framework for comparing algorithms based on execution time.

Sentiment Analysis,Twitter,Adjective analysis,Naïve Bayes,Linear SVM

[1]. Witte, R., Li, Q., Zhang, Y., et al.: ‘Text mining and software engineering: an integrated source code and document analysis approach’, IET Softw., 2008, 2,(1), pp. 3–16.
[2]. Delen, D., Cross land, M.D.: ‘Seeding the survey and analysis of research literature with text mining’, Expert Syst. Appl., 2008, 34, pp. 1707–1720.
[3]. Marine-Roig, E., Anton Clavé, S.: ‘Tourism analytics with massive user generated content: a case study of Barcelona’, J. Destination Mark. Manage. 2015, 4, pp. 1–11.
[4]. ‘Twitter Official Webpage’, 2016. Available at https://about.twitter.com/company, Accessed: March, 2016.
[5]. Hotho, A., Andreas, N., Paaß, G., et al.: ‘A brief survey of text mining’, LDV Forum – GLDV J. Comput. Linguist. Lang. Technol., 2005, 20, pp. 1–37.
[6]. Feldman, R., Dagan, I.: ‘Knowledge discovery in textual databases (KDT)’. Int. Conf. Knowledge Discovery and Data Mining (KDD), 1995, pp. 112–117. Available at http://www.aaai.org/Papers/KDD/1995/KDD95-012.pdf, Accessed: March 2016.
[7]. Shi, G., Kong, Y.: ‘Advances in theories and applications of text mining’. Int. Conf. Information Science and Engineering (ICISE2009), 2009, pp. 4167–4170.
[8]. Sivarajah, Uthayasankar, Zahir Irani, and Vishanth Weerakkody, "Evaluating The Use And Impact of Web 2.0 Technologies in Local Government," Government Information Quarterly. Elsevier, pp. 473–487, 2015.
[9]. Magdalini Eirinaki, Shamita Pisal, and Japinder Singh, “Feature based opinion mining and ranking,” Journal of Computer and System Sciences, vol.78, pp. 1175–1184, 2012.
[10]. Kushal Bafna, and Durga Toshniwal, “Feature Based Summarization of Customers` Reviews of Online Products,” in proc. 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems –KES, vol. 22, pp. 142-151,2013.
[11]. Minqing Hu, and Bing Liu, “Mining and Summarizing Customer Reviews,” Association for Computing Machinery -ACM, pp. 168-177, 2004.
[12]. Edison Marrese-Taylor, Juan D. Velásquez, and Felipe Bravo-Marquez, “A novel deterministic approach for aspect-based opinion mining in tourism products reviews,” Expert Systems with Applications, vol. 41, pp. 7764–7775, 2014.
[13]. Changqin Quan, and Fuji Ren, “Unsupervised product feature extraction for feature-oriented opinion determination,” Information Sciences, vol. 272, pp. 16–28, 2014.
[14]. Zhijun Yan, Meiming Xing, Dongsong Zhang, and Baizhang Maa, “EXPRS: An extended pagerank method for product feature extraction nbbn from online consumer reviews,” Information & Management, vol. 52, pp. 850–858, 2015.
[15]. Ayoub Bagheri, Mohamad Saraee, and Franciska de Jong, “Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews,” Knowledge Based Systems, vol.52, pp. 201–213, 2013.
[16]. Xiaohui Yu, Yang Liu, Jimmy Xiangji Huang, Aijun An, “Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA, vol. 24, No.4, APRIL 2012.
[17]. Andrei Oghina, Mathias Breuss, Manos Tsagkias & Maarten de Rijke. (2012) Predicting IMDB movie ratings using social media. Proceedings of the 34th European conference on Advances in Information Retrieval, pp. 503-507.
[18]. U.V Kulkarni, S.V Shinde, “Neuro –fuzzy classifier based on the Gaussian membership function”, 4th ICCCNT 2013, July 4-6, 2013, Tiruchengode, India.
[19]. Vikramaditya Jakkula, “Tutorial on Support Vector Machine” ,2013.
[20]. Shahrukh Teli M-Tech Student, Prashasti Kanikar Assistant Professor, MPSTME SVKM’SNMIMS University, Mumbai, India.“A Survey on Decision Tree Based Approaches in Data Mining”, 2015.
[21]. Lan Li, Shaobin Ma, Yun Zhang, “Optimization Algorithm based on Support Vector Machine” in Seventh International Symposium on Computational Intelligence and Design, 2014.
[22]. Duric Adnan, Song Fei., “Feature selection for sentiment analysis based on content and syntax models”, Decis Support Syst, 53:704–11, 2012.
[23]. Hemnaath, R., and Low, B.W. “Sentiment Analysis Using Maximum Entropy and Support Vector Machine.” Semantic Technology and Knowledge Engineering, 2010.
[24]. L. Jiang, M. Yu, M. Zhou, X. Liu and T. Zhao, "Target dependent twitter sentiment classification", Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 151--160, 2011.
[25]. L. Chen, C. Liu and H. Chiu, "A neural network based approach for sentiment classification in the blogosphere", Journal of Informatics, vol. 5, no. 2, pp. 313-322, 2011.
[26]. M. Anjaria and R. Guddeti, "Influence factor based opinion mining of Twitter data using supervised learning", Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on, pp. 1--8, 2014.
[27]. A. Barhan and A. Shakhomirov, "Methods for Sentiment Analysis of Twitter Messages", 12th Conference of FRUCT Association, 2012.