An Integrated Approach for Amazon Product Reviews Classification Using Sentiment Analysis
Authors: Deepika Kumar, Ritik Aggarwal, Siddhant Raghuvanshi, Shourya Chand, India
Sentiment Analysis plays a huge role in business analytics and situations in which text needs to be analyzed. It is used in anticipating market progression based on different news, online blogs and social media opinions. Essential part of information-gathering for market research is to find the opinion of people about the product. Many business enterprises are utilizing these opinions to perform better in the market. In this paper, the analysis is done on the Amazon product?s reviews dataset. The data is organized through preprocessing and after cleaning through various techniques, some useful features are selected and sentiment analysis is done to generate a sentiment polarity. Various different learning techniques like Naïve Bayes, Linear Support Vector Machine and Logistic Regression classifiers are applied on the preprocessed data and comparison analysis is done to find the best classifier fit for the reviews data through the detailed analysis and generation of the Receiver operator characteristics curve and the comparison analysis through AUC value of different classifiers testing the precision or the accuracy. A term-doc incidence matrix is built using term frequency and inverse document frequency ponderation. The results obtained from the integrated approach of combining the sentiment polarity with the ratings to recommend a particular Amazon product proves invaluable for both the customer and seller.
Keywords: Data Preprocessing, NLP, Sentiment Analysis, Classification
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