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  Sentiment Analysis: Opinion Mining of Positive, Negative or Neutral Twitter Data Using Hadoop  
  Authors : Komal Sutar; Snehal Kasab; Sneha Kindare; Pooja Dhule
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Social Networking Service (SNS), is a platform to provide social relations among individuals who share common interest. Twitter has become very popular. Millions of users post their comments on twitter; they specify their view on current affairs. Daily large amount of row data is available and which can be helpful for industrial or business purpose. Hence the twitter data can be analyzed and used for different businesses which will helpful for decision making. This paper gives a way of analysis of twitter data using AFFIN, EMOTICON for natural language processing. To store, categories & process large sentiments we are using Hadoop an open source framework.


Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 177-180

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Publication Link : Sentiment Analysis: Opinion Mining of Positive, Negative or Neutral Twitter Data Using Hadoop




Komal Sutar : Computer Department, SPPU, India

Snehal Kasab : Computer Department, SPPU, India

Sneha Kindare : Computer Department, SPPU, India

Pooja Dhule : Computer Department, SPPU, India








Sentiment Analysis

Stanford NLP



Twitter4j API

In this paper, we introduced a new technique to do sentiment analysis of twitter data. It will give us an effective output which is easy to understand. This application is very useful for decision making in various domains. And because of HADOOP it becomes easy to process the data in less time.










[1] Turney, P. (2002). Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews ACL. [2] Pang, B. and Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity analysis using subjectivity summarization based on minimum cuts ACL. [3] Yu, H. and Hatzivassiloglou, V. (2003). Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. Conference on Empirical methods in natural language processing, 10:129–136. [4] Kim, S. M. and Hovy, E. (2004) Determining the sentiment of opinions. Coling. [5] Wilson, T., Wiebe, J., and Hoffman, P. (2005). Recognizing contextual polarity in phrase level sentiment analysis. ACL. [6] Akkaya, Cem, Janyce Wiebe, and Rada Mihalcea 2009. Subjectivity word sense disambiguation. In EMNLP. [7] Wiebe, Janyce M. 2000. Learning subjective adjectives from corpora. In AAAI. [8] Riloff, Ellen. 2003. Learning extraction patterns for subjective expressions. In EMNLP. [9] Whitelaw, Casey, Navendu Garg, and Shlomo Argamon. 2005. Using appraisal groups for sentiment analysis. In CIKM. [10] Mihalcea, Rada and Hugo Liu. 2006. A corpusbased approach to finding happiness. In AAAI 2006 Symposium on Computational Approaches to Analysing Weblogs. AAAI Press. [11] Mishne, Gilad. 2005. Experiments with mood classification in blog posts. In Proceedings of the 1st Workshop on Stylistic Analysis Of Text. [12] Go, A., Bhayani, R., and Huang, L. (2009). Twitter sentiment classification using distant supervision. Technical report, Stanford. [13] Alexander Pak and Patrick Paroubek. 2010. Twitter as a corpus for sentiment analysis and opinion mining.Proceedings of LREC. [14] Davidov, D., O. Tsur, and A. Rappoport. 2010. Semisupervised recognition of sarcastic sentences in twitter and amazon. In CoNLL [15] Apoorv Agarwal Boyi Xie Ilia Vovsha Owen Rambow Rebecca Passonneau ”Sentiment Analysis of Twitter Data” Department of Computer Science Columbia University New York, NY 10027 USA fapoorv@cs, xie@cs, iv2121@, rambow@ccls, becky@csg.columbia.edu 2011 [16] Sunil B. Mane, YashwantSawant, SaifKazi, VaibhavShinde ”Real Time Sentiment Analysis of Twitter Data Using Hadoop” College of Engineering, Pune International Journal of Computer Science and Information Technologies 2014 [17] Shulong Tan,Yang Li, Huan Sun, Ziyu Guan, XifengYan,Member.IEEE,JiajunBu,Member,IEEE ChunChen ,Member, IEEE, and XiaofeiHe, Member” Interpreting the Public Sentiment Variations on Twitter” , IEEE 2014 [18] Ryan M. Eshleman and Hui Yang reshlema@mail.sfsu.edu,Hey 311, come clean my street! A Spatio-temporal Sentiment Analysis of Twitter Data and 311 Civil Complaints huiyang@sfsu.edu Department of Computer Science San Francisco State University 1600 Holloway Avenue, San Francisco, CA, USA, IEEE 2014 [19] Erik Cambria Temasek Laboratories ”An Introduction to Concept-Level Sentiment Analysis” , National University of Singapore Springer-Verlag Berlin Heidelberg 2013 cambria@nus.edu.sghttp://sentic.net [20] S Anna Jurek 1,2, Yaxin Bi2, Maurice Mulvenna 2 1 RepKnight Limited, 37A Upper Dunmurry Lane, Belfast, BT17 0AJ ”Twitter Sentiment Analysis for Security-Related Information Gathering” , UK 2 School of Computing and Mathematics, Faculty of Computing and Engineering, University of Ulster, BT37 0QB, UK anna.jurek@repknight.com (jureka@ email.ulster.ac.uk),y.bi@ulster.ac.uk,md.mulvenna @ulster.ac.uk ,IEEE 2014