Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

An Approach to Integrating Sentiment Analysis into Recommender Systems

Version 1 : Received: 1 July 2021 / Approved: 2 July 2021 / Online: 2 July 2021 (15:45:36 CEST)

A peer-reviewed article of this Preprint also exists.

Dang, C.N.; Moreno-García, M.N.; Prieta, F.D. An Approach to Integrating Sentiment Analysis into Recommender Systems. Sensors 2021, 21, 5666. Dang, C.N.; Moreno-García, M.N.; Prieta, F.D. An Approach to Integrating Sentiment Analysis into Recommender Systems. Sensors 2021, 21, 5666.

Journal reference: Sensors 2021, 21, 5666
DOI: 10.3390/s21165666

Abstract

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

Keywords

sentiment analysis; deep learning; recommender system; natural language processing

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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