An Approach to Select the Best User Reviews on the Web

摘要

The indexed Web increases every day, making the development of automatic methods for knowledge extraction more relevant. The area of Sentiment Analysis or Opinion Mining aims to extract opinions from the user-generated content and to define the semantic orientation of each individual opinion. This work proposes an approach to estimate the degree of importance of comments generated by web users by using a Fuzzy system. The system has three inputs: author reputation, number of tuples (feature, quality word), and percentage of correctly spelled words and one output: importance degree of the comment. The importance degree was used to select the best comments in a Corpus. The paper also describes two experiments: the first was used to fit the system and was conducted with 350 reviews about smart-phones (168 positives and 182 negatives). It achieved 63.17% in f-measure in the top 50 positive reviews, and 43.75% in f-measure in top 50 negative reviews. The second was used to compare the results of a sentiment orientation method before and after the selection of the best comments. It was conducted with 1620 reviews also about smartphones (982 positives and 594 negatives) and our approach improved the results of sentiment orientation method up to approximately 10% in f-measure in positive reviews and 7% in f-measure in negative reviews.