An Unsupervised Approach for Feature Based Sentiment Analysis of Product Reviews
نویسندگان
چکیده
Opinion mining or sentiment analysis is the task of mining polarity of opinions which comprises of the area such as natural language processing, data mining. As Eshops grow at rapidly increasing rates, customers on web write product reviews. These product reviews are useful for a customer in his decision making process on whether to purchase the product. Thousands or Hundreds of reviews may appear for a popular product. It is difficult for a customer to read and analyze large number of reviews and form an opinion on product reviews. In order to solve this problem we use an automated approach to mine polarity of reviews. Feature based sentiment analysis focus on different aspects or features of a product. Features of camera are battery life, zoom etc. Earlier works are based on document level and sentence level opinion mining which give a polarity of the review of the product as a whole rather than considering its features. This paper focus on an unsupervised model for analysis of product reviews at feature level and combined use of co-reference resolution, dependency parsing, linguistic rules, adverb adjective combinations, adverb verb combinations and SentiWordNet together for sentiment analysis.
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