An Efficient Sentiment Analysis Approach for Product Review using Turney Algorithm

Sentiment analysis can be done by means of Classification and its most important tasks are text categorization, tone recognition, image classification etc. Mostly the extant methods of supervised classification are based on traditional statistics, which can provide ideal results. The main aim is to increase the accuracy and to report the manufacturer about the negatives of the product. The major problem is categorization of sentiment polarity, which is the problem of sentiment analysis. There are two levels of categorization and they are Review-level Categorization and Sentence-level Categorization. Categorization of review-level becomes arduous when we attempt to classify the reviews respect with their specific rating related to star-scaled. Second, Review-level Categorization has a drawback in Implicit-level sentiment analysis. Mostly SVM, Naive Bayesian and Decision Tree are mainly used to improve the efficiency of classification. Amazon Dataset is used as Dataset in proposed system to improve the accuracy of Turney algorithm. Semantic Orientation (SO) with Point wise Mutual Information yields good results than other classification methods. The review level gets subjected as positive value, on acquaintance of positive average SO. On the other hand, the review level acquires a negative level in accordance with attainment of negative average SO. (C) 2019 The Authors. Published by Elsevier B.V.