Sentiment Analysis and Opinion Summarization of Product Feedback
Sindhu C1, Vadivu G2

1Sindhu C, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur (Tamil Nadu), India.
2Vadivu G, Department of Information Technology, SRM Institute of Science and Technology, Kattankulathur (Tamil Nadu), India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 27 August 2019 | PP: 59-64 | Volume-8 Issue-2S4 July 2019 | Retrieval Number: B10110782S419/2019©BEIESP | DOI: 10.35940/ijrte.B1011.0782S419
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Abstract: With the exponential growth of online shopping platforms, user interaction is made direct through their reviews and ratings. User’s opinions and experiences are a significant source of valuable information in decision making process. In recent days, almost every website encourages users to express and exchange their views, suggestions and opinions related to product, services, policies, etc. publicly. Opinion mining is an extensive branch of Artificial Intelligence and a form of Natural Language Processing which illustrates the attitude of the customers, in specific services or products. Also known as Sentiment Analysis, it aims at determining the response and mood or attitude of the speaker or the overall contextual and emotional polarity or reaction. Existing algorithms determine sentiment by training on datasets, lexicon-based approach by calculating polarity and rule-based approach for classification. Opinion Summarization is the process of consolidating a large amount of sentiments and opinions into a clear and brief statement for an easier grasp on the underlying context. Major summarization methods include, Extractive method, Sentence Ranking, Abstractive method and Clustering of Textual Segments. Hence it is important to judge and classify these reviews and present a laconic opinion so it would be easier for users to obtain a gist and overall polarity on the various reviews instead of going through all of them.
Keywords: Feature Extraction, Opinion Mining, Opinion Summarization, Review Based Opinion Mining, Sentiment Analysis.
Scope of the Article: Software Product Lines