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Title: The role of sentiment analysis in a recommender system: a systematic survey

Authors: Jitali Patel; Hitesh Chhinkaniwala

Addresses: Nirma University – Institute of Technology, Gujarat, India ' Adani Institute of Infrastructure (AII), Ahmedabad, Gujarat, India

Abstract: Currently, fields like e-commerce, education, social media, tourism, and the entertainment industry rely on recommender systems to provide personalised services to their clients. The most common and widely accepted technique - collaborative filtering, creates recommendations by examining the users' past rating patterns. Collaborative filtering assumes that a users' past rating data accurately reflects their actual preferences. However, different study found that the ratings may not accurately reflect user preferences in the real-world circumstances. Therefore, to deal with this problem, sentiment analysis of user-generated text is started to be used. It helps to improve the performance of recommender systems, as it provides more specific and trustworthy user preferences than ratings. A sentiment-aware recommender system captures sentiment from the user-generated content and provides most suited personalised services to the user. We have classified sentiment enhanced recommender systems according to the level of sentiment analysis and presented technical aspects such as datasets, methodologies and results.

Keywords: recommender system; sentiment analysis; user-generated text; collaborative filtering; sentiment-aware recommender system.

DOI: 10.1504/IJWET.2022.125086

International Journal of Web Engineering and Technology, 2022 Vol.17 No.1, pp.29 - 62

Accepted: 18 Apr 2022
Published online: 25 Aug 2022 *

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