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Survey Paper | Computer Science & Engineering | India | Volume 4 Issue 12, December 2015

Survey on Product Review Sentiment Analysis with Aspect Ranking

Harsha Patil ^[2] | P. M. Mane ^[4]

Abstract: Millions of reviews on products are now available on internet. Consumers commonly seek for quality information from online customers review prior to make their purchasing product decision while many firms use online reviews as important feedbacks in their product development, marketing and consumer relationship management. Both consumers and firms are benefited by this rich and valuable knowledge from consumers review. When reviews on various aspects of the products are in textual format, it is difficult to identify and analyze such reviews so we are developing the system to mine those aspects and rank them which will help for better product development. We proposed product aspect ranking framework, which automatically identifies the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews. Important product aspects identification is based on two observations 1) the important aspects are usually commented on by a large number of consumers and 2) overall opinions on the product is decided by opinions on important aspects of those products. In particular, consumer reviews of a product are given in textual format, we first parse the reviews with Natural Language Processor to identify the aspects of particular product then for sentiment analysis we use sentiment classifier such as Nave Bays or SVM to classify the comments as positive and negative sentiments. After sentiment analysis we apply Probabilistic aspect ranking algorithm to conclude the importance of aspects by simultaneously considering aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions.

Keywords: consumer review, product aspects, aspect ranking, aspect identification, sentiment classification

Edition: Volume 4 Issue 12, December 2015,

Pages: 1783 - 1786

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