Sentiment Analysis on Product Reviews Using Machine Learning Techniques
Sentiment Analysis and Opinion Mining is a most popular field to analyze and find out insights from text data from various sources like Facebook, Twitter, and Amazon, etc. It plays a vital role in enabling the businesses to work actively on improving the business strategy and gain an in-depth insight of the buyer’s feedback about their product. It involves computational study of behavior of an individual in terms of his buying interest and then mining his opinions about a company’s business entity. This entity can be visualized as an event, individual, blog post or product experience. In this paper, Dataset has taken from Amazon which contains reviews of Camera, Laptops, Mobile phones, tablets, TVs, video surveillance. After preprocessing we applied machine learning algorithms to classify reviews that are positive or negative. This paper concludes that, Machine Learning Techniques gives best results to classify the Products Reviews. Naïve Bayes got accuracy 98.17% and Support Vector machine got accuracy 93.54% for Camera Reviews.
KeywordsSentiment analysis Natural language processing Product reviews Machine learning Support vector machine Naïve Bayes
The author would like to thank to Data Analytics Research Lab, Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad for providing infrastructure to carry my research work. The author acknowledges the Department of Science and Technology (DST), New Delhi, India for granting financial assistance in the form of DST INSPIRE FELLOWSHIP (JRF) during this research work.
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