IJERT-EMS
IJERT-EMS

Unsupervised Linguistic Approach for Sentiment Classification from Online Reviews Using Sentiwordnet 3.0


Unsupervised Linguistic Approach for Sentiment Classification from Online Reviews Using Sentiwordnet 3.0
Authors : Monalisa Ghosh, Animesh Kar
Publication Date: 02-09-2013

Authors

Author(s):  Monalisa Ghosh, Animesh Kar

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Vol.2 - Issue 9 (September - 2013)

e-ISSN:   2278-0181

Abstract

Sentiment analysis is an area of text classification that began early of the last decade and has recently been receiving a lot of attention from researchers. Sentiment analysis involves analyzing datasets (online review, social media, blogs, and discussion groups) which contain opinions with the objective of classifying the opinions as positive, negative, or neutral. Opinion plays essential part in our information-gathering behaviour before taking a decision. In this work we describe a simple technique to perform sentiment classification based on an unsupervised linguistic approach. Our pattern-based method applies a classification rule according to which each review is classified as positive or negative. In this paper we used SentiWordNet to calculate overall sentiment score of each sentence. The results indicate SentiWordNet could be used as an important resource for sentiment classification tasks. Additional considerations are made on possible further improvements to the method.

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