Sentiment analysis is defined as the process of mining of data, view, review or sentence to predict the emotion of the sentence through natural language processing (NLP). The sentiment analysis involve classification of text into three phase “Positive”, “Negative” or “Neutral”. It analyzes the data and labels the ‘better’ and ‘worse’ sentiment as positive and negative respectively. Thus, in the past years, the World Wide Web (WWW) has become a huge source of raw data generated custom or user. Using social media, e-commerce website, movies reviews such as Facebook, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. In WWW, where millions of people express their views in their daily interaction, either in the social media or in e-commence which can be their sentiments and opinions about particular thing. These growing raw data are an extremely high source of information for any kind of decision making process either positive or negative. To analysis of such huge data automatically, the field of sentiment analysis has turn up. The main aim of sentiment analysis is to identifying polarity of the data in the Web and classifying them. Sentiment analysis is text based analysis, but there are certain challenges to find the accurate polarity of the sentence. This states that there is need to find the better solution to get much better results than the previous approach or technique used to find polarity of sentence. Therefore, to find polarity or sentiment of, user or customer there is a demand for automated data analysis techniques. In this paper, a detailed survey of different techniques or approach is used in sentiment analysis and a new technique which is proposed in this paper.

Najma Sultana1, Pintu Kumar2, Monika Rani Patra3, Sourabh Chandra4, S K Safikul Alam5
Calcutta Institute of Technology, India1,2,3,4, Raja Ranajit Kishore Government Polytechnic, India2

Sentiment Analysis, Naïve Bayes, Mining, Support Vector Machine, Polarity, Semantic
Published By :
Published In :
ICTACT Journal on Soft Computing
( Volume: 9 , Issue: 3 )
Date of Publication :
April 2019

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