ICTACT Journal on Soft Computing

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A Framework for Aspect based Sentiment Analysis Using Fuzzy Logic


Affiliations
1 Department of Computer Science, St. Joseph’s College (Autonomous), India
     

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Sentiment Analysis (SA) is the study of people’s opinions, emotions, and appraisals toward products and events. In the past years, it fascinated a great deal of attentions from both industry and academia for a variety of applications. Opinions are significant, because people need to make decisions. It is helpful not only for the individuals but also for the business organizations. Fuzzy logic can provide a quick way to solve the haziness present in most of the natural languages. The techniques are less explored in sentiment analysis. In this paper, Aspect based Sentiment Summarization (ASFuL) is proposed with fuzzy logic by classifying opinions polarity as strong positive, positive, negative and strong negative. It also integrates the non-opinionated sentences using Imputation of Missing Sentiment (IMS) mechanism which plays a vital role in generating precise results. The researchers used Fuzzy Logic to find sentiment classes in the review. The results show that the mechanism is viable to extract opinions in an efficient manner.

Keywords

ASFuL, Sentiment Analysis, Aspect, Sentiment Summarization, Fuzzy Logic.
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  • O. Appel, F. Chiclana, J. Carter and H. Fujita, “A Hybrid Approach to Sentiment Analysis”, Proceedings of IEEE Congress on Evolutionary Computation, pp. 4950-4957, 2016.

  • Suresh Hima and S. Gladston Raj, “An Unsupervised Fuzzy Clustering Method for Twitter Sentiment Analysis”, Proceedings of IEEE International Conference on Computational Systems and Information Systems for Sustainable Solutions, pp. 80-85, 2016.

  • Giuseppe D. Aniello, Angelo Gaeta, Matteo Gaeta, Vincenzo Loia and Marek Z. Reformat, “Collective Awareness in Smart City with Fuzzy Cognitive Maps and Fuzzy sets”, Proceedings of IEEE Sponsored International Conference on Fuzzy Systems, pp. 1554-1561, 2016.

  • K. Indhuja and P.C. Reghu Raj, “Fuzzy Logic Based Sentiment Analysis of Product Review Documents”, Proceedings of 1st International Conference on Computational Systems and Communications, pp. 18-22, 2014.

  • E.E. Luneva, P.I. Banokin, A.A. Yefremov and T. Tiropanis, “Method of Evaluation of Social Network User Sentiments Based on Fuzzy Logic”, International Journal of Key Engineering Materials, Vol. 685, pp. 847-851, 2016.

  • Mauro Dragoni, Andrea G. B. Tettamanzi and Celia Da Costa Pereira, “Propagating and Aggregating Fuzzy Polarities for Concept-Level Sentiment Analysis”, Cognitive Computation, Vol. 7, No. 2, pp. 186-197, 2015.

  • V. Reshma and Ansamma John, “Aspect Based Summarization of Reviews Using Naive Bayesian Classifier and Fuzzy Logic”, Proceedings of IEEE International Conference on Control, Communication and Computing, pp. 617-621, 2015.

  • A. Jenifer Jothi Mary and L Arockiam, “IMS: Imputation of Missing Sentiment in the Aspect based Sentiment Analysis”, Proceedings of International Conference on IoT, Data Science and Security, pp. 263-273, 2017.

  • S. Zhang, C. Zhu, J.K.O. Sin and P.K.T. Mok, “A Novel Ultrathin Elevated Channel Low-Temperature Poly-Si TFT”, IEEE Electron Device Letters, Vol. 20, No. 11, pp. 569-571, 1999.

  • Jorge Ropero and Ariel Gomez, “A Fuzzy Logic Intelligent Agent for Information Extraction Introducing a New Fuzzy Logic-based Term Weighting Scheme”, Expert Systems with Applications, Vol. 39, No. 4, pp. 4567-458, 2012.

  • A Fuzzy Implementation, http://apps.ensic.inpl-nancy.fr/benchmarkWWTP/RiskAnalysis/RiskWeb/RiskModule_070423_fichiers/Fuzzy_implementation_070423.pdf.

  • B.N. Supriya, V. Kallimani, S. Prakash and C.B. Akki, “Twitter Sentiment Analysis Using Binary Classification Technique”, Proceedings of International Conference on Nature of Computation and Communication, pp. 391-396, 2016.


Abstract Views: 548

PDF Views: 3




  • A Framework for Aspect based Sentiment Analysis Using Fuzzy Logic

Abstract Views: 548  |  PDF Views: 3

Authors

A. Jenifer Jothi Mary
Department of Computer Science, St. Joseph’s College (Autonomous), India
L. Arockiam
Department of Computer Science, St. Joseph’s College (Autonomous), India

Abstract


Sentiment Analysis (SA) is the study of people’s opinions, emotions, and appraisals toward products and events. In the past years, it fascinated a great deal of attentions from both industry and academia for a variety of applications. Opinions are significant, because people need to make decisions. It is helpful not only for the individuals but also for the business organizations. Fuzzy logic can provide a quick way to solve the haziness present in most of the natural languages. The techniques are less explored in sentiment analysis. In this paper, Aspect based Sentiment Summarization (ASFuL) is proposed with fuzzy logic by classifying opinions polarity as strong positive, positive, negative and strong negative. It also integrates the non-opinionated sentences using Imputation of Missing Sentiment (IMS) mechanism which plays a vital role in generating precise results. The researchers used Fuzzy Logic to find sentiment classes in the review. The results show that the mechanism is viable to extract opinions in an efficient manner.

Keywords


ASFuL, Sentiment Analysis, Aspect, Sentiment Summarization, Fuzzy Logic.

References