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Emoticon based Sentiment Analysis using Parallel Analytics on Hadoop


  • Department of IT, St. Joseph’s College (Autonomous), College Road, Tiruchirappalli - 620002, Tamil Nadu, India
  • Jamal Mohamed College, 7, Race Course Road, Khajanagar, Tiruchirappalli – 620020, Tamil Nadu, India


Objectives: The major objective of this approach is to provide a sentiment analysis architecture that can operate on streaming big data to provide effective results at tolerable time limits. Method/Analysis: An effective mechanism for analyzing the social networking messages to identify the sentiment levels has been proposed. This is a generic model that can be used for product or organization specific analysis. Further, this method also considers emoticons, which form the integral part of any expressed emotion. The entire process is carried out in Hadoop Architecture using the MapReduce paradigm. Findings: Experiments have been conducted on a Hadoop cluster. Inputs were passed from a client node connected to the cluster. Map Reduce programs were executed in six phases, each phase performing a single task in map and reduce phases. The ROC plot exhibits excellect accuracies with most of the points being clustered in the top left region, some even approaching 100% effectiveness. Even the PR plots exihibits similar efficiency scenario with high positive retrieval rates. Incorporating the emoticons plays a major role in increasing the efficiency of this approach. Novelty/Improvement: This approach uses Hadoop based implementations, involving Map and Reduce operations. Using this approach provides data scalability and improves the efficiency of the results in acceptable time limits.


Emoticons, Hadoop, Map Reduce, Polarity Identification, Sentiment Analysis, Social Networking Data Processing.

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  • Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems. 2015 Nov 30; 89:14-46.
  • da Silva NF, Hruschka ER, Hruschka ER. Tweet sentiment analysis with classifier ensembles. Decision Support Systems. 2014 Oct 31; 66:170-9.
  • Baecchi C, Uricchio T, Bertini M, Del Bimbo A. A multimodal feature learning approach for sentiment analysis of social network multimedia. Multimedia Tools and Applications. 2015; p. 1-9.
  • Balahur A, Perea-Ortega JM. Sentiment analysis system adaptation for multilingual processing: The case of tweets. Information Processing & Management. 2015 Jul 31; 51(4):547-56.
  • Saif H, He Y, Fernandez M, Alani H. Contextual semantics for sentiment analysis of Twitter. Information Processing & Management. 2016 Jan 31; 52(1):5-19.
  • Katz G, Ofek N, Shapira B. ConSent. Knowledge-Based Systems. 2015 Aug 1; 84(C):162-78.
  • Korenek P, Simko M. Sentiment analysis on microblog utilizing appraisal theory. World Wide Web. 2014 Jul 1; 17(4):847-67.
  • Ruba KV, Venkatesan D. Building a Custom Sentiment Analysis Tool based on an Ontology for Twitter Posts. Indian Journal of Science and Technology. 2015 Jul; 8(13):1-9.
  • Zol S, Mulay P. Analyzing Sentiments for Generating Opinions (ASGO)-A New Approach. Indian Journal of Science and Technology. 2015 Feb; 8(S4):1-6.
  • Mahajan C, Mulay P. E3: Effective Emoticon Extractor for Behavior Analysis from Social Media. Procedia Computer Science. 2015 Dec 31; 50:610-6.
  • Litvinova TA, Seredin PV, Litvinova OA. Using Part-of-Speech Sequences Frequencies in a Text to Predict Author Personality: a Corpus Study. Indian Journal of Science and Technology. 2015 May; 8(S9):1-5.
  • Zhu C, Zhu H, Ge Y, Chen E, Liu Q, Xu T, Xiong H. Tracking the evolution of social emotions with topic models. Knowledge and Information Systems. 2015 Jul 28; p. 1-28.
  • Chaumartin FR. UPAR7: A knowledge-based system for headline sentiment tagging. Association for Computational Linguistics: Proceedings of the 4th International Workshop on Semantic Evaluations , PA. 2007 Jun 23; p. 422-25.
  • Kozareva Z, Navarro B, Vazquez S, Montoyo A. UA-ZBSA: a headline emotion classification through web information. Association for Computational Linguistics: Proceedings of the 4th International Workshop on Semantic Evaluations, Spain. 2007 Jun 23; p. 334-37.
  • Lin KH, Yang C, Chen HH. What emotions do news articles trigger in their readers? ACM: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval NY. 2007 Jul 23; p. 733-34.
  • Bao S, Xu S, Zhang L, Yan R, Su Z, Han D, Yu Y. Joint emotion-topic modeling for social affective text mining. ICDM’09, Ninth IEEE International Conference on Data Mining, 2009. IEEE, 2009 Dec 6; p. 699-704.
  • Reyes A, Rosso P, Veale T. A multidimensional approach for detecting irony in twitter. Language resources and evaluation. 2013 Mar 1; 47(1):239-68.
  • Kreuz R. Using figurative language to increase advertising effectiveness. University of Memphis, Memphis, TN: Office of Naval Research Military Personnel Research Science Workshop. 2001 Jun 4.
  • Kumon-Nakamura S, Glucksberg S, Brown M. How about another piece of pie: The allusional pretense theory of discourse irony. London: Taylor and Francis Group: Gibbs R, Colston H (Eds.). Irony in language and thought. 2007; p. 57–96.
  • Lucariello J. Situational irony: A concept of events gone away. Irony in language and thought. 2007; p. 467-98.
  • Shukla A, Chaudhary BD. A study of usage of symbols and opinionated words in annotation for modeling literature survey experiences. Education and Information Technologies. 2015 Mar 1; 20(1):91-111.
  • Xiong X, Zhou G, Huang Y, Chen H, Xu K. Dynamic evolution of collective emotions in social networks: a case study of Sinaweibo. Science China Information Sciences. 2013 Jul 1; 56(7):1-8.
  • Feng S, Song K, Wang D, Yu G. A word-emoticon mutual reinforcement ranking model for building sentiment lexicon from massive collection of microblogs. World Wide Web. 2015 Jul 1; 18(4):949-67.
  • Saif H, Fernandez M, He Y, Alani H. Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold. 2013; p. 1-13.


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