An Optimal Aggregation of Product Data using Vector Space Model

In the current scenario there exists many versions of a particular product. A product might be laptop, mobile or any other gadget. With increase in number of versions there is a need to analyze the reason for release of the new version of the product. This can be done by the study of reviews and ratings provided by consumers. To get a more accurate output we first relate the rating and review usingSentiment Analysis (SA). SA is a form of text mining that helps us to understand the attitude and behavior of a customer towards a product/service. The ratings given by the customer may not be in the same level of agreement as in the review text. Customer may have issues with the product and has explained in the review but can be generous and give decent rating, such circumstances often depends on the emotional quotient of the customer. Therefore, there is a need for a system which can elicit the polarity among the reviews and check if there is proper agreement between the ratings and reviews given by user till the product become obsolete.In order to provide the correlation between the ratings and reviews lexicon method of sentiment analysis is used to generate the sentiment score for each review. Based on the sentiment score obtained the reviews are further classified into extreme negative, negative, neutral, positive, and extreme positive and compared to the ratings given by the customer. With this reviews as input, feature selection is done using vector space model. The output obtained depicts the success factors and failures of a product which helps to build a better version.