PREDICTING POLARITY USING SENTIMENTAL ANALYSIS

Abstract

With the speedy enlargement of  e-commerce over the past years, individuals share their thoughts and experiences through review. Daily lots of reviews area unit generated within the web a few product. Due to their high variety and large size it’s terribly troublesome to handle and perceive those reviews. To research these reviews and to form a choice may be a difficult task. To beat this downside an automatic opinion mining approach is required. Sentiment analysis is such a hunt space which permits a laptop to know what’s being previously searched by the user. Understanding people’s emotions is important for businesses because customers are able to express their thoughts and feelings through reviews or feedback. Sentimental analysis uses totally different techniques to work out the emotions of a text or sentence and extracts the opinion from the given text or review. As a measurement of opinions and affective states, a sentiment score generally consists of two aspects: polarity and intensity. A general method for sentiment polarity categorization (e.g. a positive, negative or neutral) is projected with careful process. Aspect based sentimental analysis for example "The battery life of this camera is too short" which says the particular aspects or features of the product. It is also the process of finding the emotional tone behind a series of words, helps in understanding the opinions and emotions given in an online mention. Can detect the sentiments of the customers through many algorithms like SVM, Naive   Bayes etc….