An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

In today’s society, sentiment analysis has gained due importance as it provides useful information about products that areused by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at anearly stage. It thus intimates users’ perception and charts out a path that is beneficial for the market to grow as a whole.Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of thedetailed components based products and their associated features, a novel method is desired to meet these criteria. So far,no such method is explored that analyses the product’s components and their features simultaneously, on the basis ofsentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis ofproduct reviews while formulating a tree structure of product, components, and associated features. In addition, evaluationof double negative sentences, detecting questions and emotions from the review sentences are measured which increasesefficiency of the FBA method. The comparison of product’s components reviews is done with other existing algorithmsTF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.