Published April 10, 2021 | Version v1
Journal article Open

Ternary Classification of Product Based Reviews: Survey, Open Issues and New Approach for Sentiment Analysis

  • 1. Department of Computer Science and Eengineering, Lakshmi Narain College of Technology, University, Bhopal, India.
  • 1. Publisher

Description

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.

Files

B1008021221.pdf

Files (416.8 kB)

Name Size Download all
md5:50eb1e73ebfa0bc6a267af746add61f5
416.8 kB Preview Download

Additional details

Related works

Is cited by
Journal article: 2582-7626 (ISSN)

Subjects

ISSN
2582-7626
Retrieval Number
100.1/ijainn.B1008021221