Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis 
Sushila Sonare1, Megha Kamble2
1Sushila Sonare, Department of Computer Science and Eengineering, Lakshmi Narain College of Technology, University, Bhopal, India.
2Dr. Megha Kamble, Department Computer Science and Engineering, Lakshmi Narain College of Technology, University, Bhopal, India.
Manuscript received on 15 March 2021 | Revised Manuscript received on 23 March 2021 | Manuscript Accepted on 15 April 2021 | Manuscript published on 30 April 2021 | PP: 1-8 | Volume-1 Issue-2, April 2021 | Retrieval Number: 100.1/ijainn.B1008021221 | DOI: 10.54105/ijainn.B1008.041221
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now-a-days, it is very common that the customers share their thoughts about any product, brand and their experience in social media. The analysts collect these reviews and process it, to extract meaningful information about the product. The beauty of social media is, it’s involved in all the domains. So the analysts got reviews from different social media and platforms for almost all kind of thing. The Sentiment Analysis is applied to predict outcomes for getting useful information, for ex.; like predict the blockbuster for a movie, rating for any new launches and many more. This type of prediction is really helpful for the customer to buy any goods or take any services in this competitive world. This paper is focused on e-commerce website reviews which are normally in text form with some special characters and some symbols (emojis). Each word in this text set got some meaning in terms of context, emotion and prior experience. These characteristics contribute to some of the features of text data for prediction. The objective of this paper is to compile existing research works on text analysis and emotion based analysis. The open issues and challenges of document based sentiment analysis are also discussed. The paper concluded with proposing a new approach of multi class classification. Ternary classification for classes positive, negative and neutral is suggested primarily for product based text and emoji reviews on Twitter social media.
Keywords: Sentiment Analysis, Reviews, Machine learning, e-commerce, Real time.
Scope of the Article: Natural Language Processing