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WIREs Data Mining Knowl Discov
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An enhanced feature‐based sentiment analysis approach

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Abstract In the last few years, online reviews where individuals express their thoughts, interests, experiences, and opinions have broadly spread over the internet. Sentiment analysis has evolved to analyze these online reviews and provide valuable insights for both individuals and organizations that may help them in making decisions. Unfortunately the performance of sentiment analysis process is affected by the nature of online reviews' content that may contain emoticons and negation words. Moreover, spam reviews have been written for the purpose of deceiving others. Therefore, there is a need to develop an approach that considers these issues. In this paper, an enhanced approach for sentiment analysis is proposed which aims to enhance the performance of classifying reviews based on their features and assigning accurate sentiment score to features. This enhanced approach is achieved by handling negation, detecting emoticons, and detecting spam reviews using a combination of different types of properties which leads to achieving better predictive performance. The proposed approach has been verified against three datasets of different sizes. The results indicate that the proposed approach achieves a maximum accuracy of about 99.06% in detecting spam reviews and a maximum accuracy of about 97.13% in classifying reviews. This article is categorized under: Algorithmic Development > Text Mining Technologies > Classification Technologies > Machine Learning
Overview of the proposed approach
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(a) Performance of Feature‐based Sentiment Analysis (FbSA) on “DOSC” dataset. (b) Performance of FbSA on “YelpNYC” dataset. (c) Performance of FbSA on “YelpZIP” dataset
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(a) Performance of spam reviews detection techniques on “DOSC” dataset. (b) Performance of spam reviews detection techniques on “YelpNYC” dataset. (c) Performance of spam reviews detection techniques on “YelpZIP” dataset
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Overview of the different types of properties used for spam reviews detection
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Technologies > Machine Learning
Technologies > Classification
Algorithmic Development > Text Mining

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