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Sentiment Analysis for Product Review


Affiliations
1 Department of Computer Science and Engineering, Calcutta Institute of Technology, India
2 Department of Computer Science and Technology, Raja Ranajit Kishore Government Polytechnic, India
     

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Sentiment analysis is defined as the process of mining of data, view, review or sentence to predict the emotion of the sentence through natural language processing (NLP). The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. It analyzes the data and labels the ‘better’ and ‘worse’ sentiment as positive and negative respectively. Thus, in the past years, the World Wide Web (WWW) has become a huge source of raw data generated custom or user. Using social media, e-commerce website, movies reviews such as Facebook, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. In WWW, where millions of people express their views in their daily interaction, either in the social media or in e-commence which can be their sentiments and opinions about particular thing. These growing raw data are an extremely high source of information for any kind of decision making process either positive or negative. To analysis of such huge data automatically, the field of sentiment analysis has turn up. The main aim of sentiment analysis is to identifying polarity of the data in the Web and classifying them. Sentiment analysis is text based analysis, but there are certain challenges to find the accurate polarity of the sentence. This states that there is need to find the better solution to get much better results than the previous approach or technique used to find polarity of sentence. Therefore, to find polarity or sentiment of, user or customer there is a demand for automated data analysis techniques. In this paper, a detailed survey of different techniques or approach is used in sentiment analysis and a new technique which is proposed in this paper.

Keywords

Sentiment Analysis, Naïve Bayes, Mining, Support Vector Machine, Polarity, Semantic.
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  • Samaneh Moghaddam and Martin Ester, “Opinion Digger: An Unsupervised Opinion Miner from Unstructured Product Reviews”, Proceedings of 19th ACM International Conference on Information and Knowledge Management, pp. 1825-1828, 2010.

  • Aurangzeb Khan, Baharum Baharudin and Khairullah Khan, “Sentiment Classification from Online Customer Reviews using Lexical Contextual Sentence Structure”, Proceedings of International Conference on Software Engineering and Computer Systems, pp. 317-331, 2011.

  • M. Hu and B. Liu, “Mining and Summarizing Customer Reviews”, Proceedings of 10th ACM International Conference on Knowledge Discovery and Data Mining, pp. 166-177, 2005.

  • A.M. Popescu and O. Etzioni, “Extracting Product Features and Opinions from Reviews”, Proceedings of International Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 339-346, 2005.

  • G. Vinodhini and R.M. Chandrasekaram, “Sentiment Analysis and opinion Mining: A Survey”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 6, pp. 28-35, 2012.

  • A. Collomb, C. Costea, D. Joyeux, O. Hasan and L. Brunie, “A Study and Comparison of Sentiment Analysis Methods for Reputation Evaluation”, Available at: https://liris.cnrs.fr/Documents/Liris-6508.pdf.

  • Mika V. Mantyla, Daniel Graziotin and Miikka Kuutila, “The Evolution of Sentiment Analysis-A Review of Research Topics”, Computer Science Review, Vol. 27, No. 1, pp. 16-32, 2018.

  • F. Benamara, C. Cesarano and D. Reforgiato, “Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone”, Proceedings of International Conference on Weblogs and Social Media, pp. 1-7, 2006.

  • R.A. Hummel and S.W. Zucker, “On the Foundations of Relaxation Labeling Processes”, Proceedings of International Conference on Computer Vision: Issues, Problems, Principles, and Paradigms, pp. 585-605, 1987.

  • Samaneh Moghaddam and Martin Ester, “ILDA: Interdependent LDA Model for Learning Latent Aspects and their Ratings from Online Product Reviews”, Proceedings of 34th International ACM Conference on Research and Development in Information Retrieval, pp. 665-674, 2011.

  • Jorge Carrillo De Albornoz, Laura Plaza, Pablo Gervas and Alberto Diaz, “A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating”, Proceedings of International Conference on Advances in Information Retrieval, pp. 55-66, 2011.

  • Sentiment Analysis, Available at: https://insightsatlas.com/sentiment-analysis/

  • Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng and Christopher Potts, “Learning Word Vectors for Sentiment Analysis”, Proceedings of 49th Annual Meeting of the Association for Computational Linguistics, pp. 1-7, 2011.

  • Understanding Sentiment Analysis: What It Is and Why It’s Used Understanding Sentiment Analysis: What It Is and Why It’s Used, Available at: https://www.brandwatch.com/blog/understanding-sentiment-analysis/

  • Sentiment Analysis Explained, Available at: https://www.lexalytics.com/technology/sentiment-analysis


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  • Sentiment Analysis for Product Review

Abstract Views: 233  |  PDF Views: 0

Authors

Najma Sultana
Department of Computer Science and Engineering, Calcutta Institute of Technology, India
Pintu Kumar
Department of Computer Science and Engineering, Calcutta Institute of Technology, India
Monika Rani Patra
Department of Computer Science and Engineering, Calcutta Institute of Technology, India
Sourabh Chandra
Department of Computer Science and Engineering, Calcutta Institute of Technology, India
S. K. Safikul Alam
Department of Computer Science and Technology, Raja Ranajit Kishore Government Polytechnic, India

Abstract


Sentiment analysis is defined as the process of mining of data, view, review or sentence to predict the emotion of the sentence through natural language processing (NLP). The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. It analyzes the data and labels the ‘better’ and ‘worse’ sentiment as positive and negative respectively. Thus, in the past years, the World Wide Web (WWW) has become a huge source of raw data generated custom or user. Using social media, e-commerce website, movies reviews such as Facebook, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. In WWW, where millions of people express their views in their daily interaction, either in the social media or in e-commence which can be their sentiments and opinions about particular thing. These growing raw data are an extremely high source of information for any kind of decision making process either positive or negative. To analysis of such huge data automatically, the field of sentiment analysis has turn up. The main aim of sentiment analysis is to identifying polarity of the data in the Web and classifying them. Sentiment analysis is text based analysis, but there are certain challenges to find the accurate polarity of the sentence. This states that there is need to find the better solution to get much better results than the previous approach or technique used to find polarity of sentence. Therefore, to find polarity or sentiment of, user or customer there is a demand for automated data analysis techniques. In this paper, a detailed survey of different techniques or approach is used in sentiment analysis and a new technique which is proposed in this paper.

Keywords


Sentiment Analysis, Naïve Bayes, Mining, Support Vector Machine, Polarity, Semantic.

References