Sentimental Analysis of Product Based Reviews Using Machine Learning Approaches
Duvvuru Mahammad Dawood Khan, Mr. Vaddi Narasimha Swamy
Machine Learning, Semantic Orientation, Sentiment Analysis, Support Vector Machine, Naïve Bayes.
With the fast growth of e-commerce, large number of products is sold online, and a lot more people are purchasing products online. People while buying also give feedback of product purchased in form of reviews. The user generated reviews for products and services are largely available on internet. Since information available on internet is so widespread we need to extract the needful information for which we make use of sentimental analysis. Sentiment analysis extracts abstract and to the point information required for source materials by applying concept of Natural language processing. It is used to deal with identification and aggregation of the opinions given by the customers. These reviews play vital role in determining potential customer for the products as well as market trend for product. This paper provides summary of reviews for products by classifying these reviews as positive, negative or neutral. Information on internet is highly Since reviews are highly unstructured, machine learning approaches are applied including naïve Bayes and support vector machine algorithms by first taking inputs as unstructured product reviews, performs preprocessing, calculates polarity of reviews, extracts features on to which comments are made and also plots graph for the result. The algorithms precision, recall and accuracy are measured Finally
Article Details
Unique Paper ID: 153553

Publication Volume & Issue: Volume 8, Issue 7

Page(s): 467 - 473
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