Abstract
Determining the polarity and strength of opinions is an important research area over the last few years. The work challenges on opinion sentences and opinion holder extraction, opinion polarity judgment and also to measure the strength of polarity. In this paper we propose a convenient way using fuzzy techniques for analyzing opinion content in a review; our main goal is to analyze and to evaluate the sentiment in the review into a score for decision-making. The web contains product reviews and consumers are often forced to wade through many on-line reviews in order to make a product choice. We use techniques that decompose the review sentences and evaluate the individual characteristics of a product. Our task is performed in three steps: (1) mining product features that have been commented by customers; (2) identifying opinion sentences in each review and extracting the opinion phrases in each opinion sentence; (3) to measure the strength of opinion phrases to summarize the results. This paper introduces FOM (Fuzzy Opinion Miner), a supervised opinion orientation detection system that mines reviews to build a model of important product features, their evaluation by reviewers and the over all importance of the reviews.
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