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An Approach to Integrating Sentiment Analysis into Recommender Systems by Cach N. Dang; María N. Moreno-García; Fernando De la Prieta is a Engineering article available to read on EtoBox.

What is An Approach to Integrating Sentiment Analysis into Recommender Systems about?

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 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 ba

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Author

Cach N. Dang; María N. Moreno-García; Fernando De la Prieta

Publisher

MDPI AG

Published

2021

Language

EN

Field

Engineering (Physical Sciences)