An Approach to Integrating Sentiment Analysis into Recommender Systems.

Cach N Dang,Fernando De La Prieta,María N Moreno-García

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 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.

Highlights

We performed experiments with two different settings without/with sentiment analysis. In the former, recommendations are based on recommender system methods without sentiment while in the second, the result of performing sentiment analysis on the reviews is incorporated to the recommendation process
The results show that the performances of the hybrid models are encouraging, with accuracy and F-score over 80% and Area Under Curve (AUC) over els for sentiment analysis: CNN and LSTM as well as LSTM and CNN, referred to as CLSTM, L-CNN, respectively
We have proposed an application of sentiment analysis in recommender systems that is based on hybrid deep-learning models and collaborative filtering on online social networks

Summary

Introduction

Several studies are summarized and discussed in [1] regarding the benefits of sentiment analysis in obtaining feedbacks and determining the interests and opinions of customers. Sentiment analysis is very useful in a wide range of application domains, including business, government, and education. Sentiment analysis can be performed on three levels of extraction: the sentence level; the document level; and the aspect or feature level. It is a process of extracting information about an entity and automatically identifying any of the subjectivities of that entity. The aim is to determine whether text generated by users conveys their positive, negative, or neutral opinions.

Methods

Results

Conclusion