A Comparative Study on Sentiment Analysis


Authors : Anbarasi M, Ayush Patel, Sarthak Panigrahi.

Volume/Issue : Volume 3 - 2018, Issue 3 - March

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/NLtX3f

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

Collecting data from manifold sources of compiled consumer reviews, a framework can be constituted to compare and analyze customer opinions. Sentiment analysis is the process of identifying emotions and attitudes of a writer towards a particular topic, product, service etc. Customers place their reliance on the text reviews in form of experiences and opinions regarding any product available with an enterprise. The main process behind sentiment analysis is categorizing texts in order to find the writer’s emotions. Sentiment analysis has a huge number of applications in a variety of fields. This technique can be very helpful to determine the popularity of a product in the online selling business and can play a major role in making decisions for companies such as amazon, flipkart, e-bay etc. It can also be used in social media sites like twitter, Facebook to identify the attitude of people towards different celebrities. This paper performs a comparative study on the different approaches used for sentiment analysis.

Keywords : k-means,sentiment analysis,SVM, Big data, Bayes classifier.

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