Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis

Ikram Karabila (), Nossayba Darraz, Anas El-Ansari, Nabil Alami and Mostafa El Mallahi
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Ikram Karabila: IPI Laboratory, Department of Mathematics and Computer Sciences, High Normal School, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
Nossayba Darraz: IPI Laboratory, Department of Mathematics and Computer Sciences, High Normal School, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco
Anas El-Ansari: MASI Laboratory, Computer Science Department, Polydisciplinary Faculty, Mohammed First University, Nador 62000, Morocco
Nabil Alami: MASI Laboratory, Higher School of Technology, Mohammed First University, Nador 62000, Morocco
Mostafa El Mallahi: IPI Laboratory, Department of Mathematics and Computer Sciences, High Normal School, Sidi Mohamed Ben Abdellah University, Fez 30000, Morocco

Future Internet, 2023, vol. 15, issue 7, 1-21

Abstract: Recommendation systems (RSs) are widely used in e-commerce to improve conversion rates by aligning product offerings with customer preferences and interests. While traditional RSs rely solely on numerical ratings to generate recommendations, these ratings alone may not be sufficient to offer personalized and accurate suggestions. To overcome this limitation, additional sources of information, such as reviews, can be utilized. However, analyzing and understanding the information contained within reviews, which are often unstructured data, is a challenging task. To address this issue, sentiment analysis (SA) has attracted considerable attention as a tool to better comprehend a user’s opinions, emotions, and attitudes. In this study, we propose a novel RS that leverages ensemble learning by integrating sentiment analysis of textual data with collaborative filtering techniques to provide users with more precise and individualized recommendations. Our system was developed in three main steps. Firstly, we used unsupervised “GloVe” vectorization for better classification performance and built a sentiment model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Secondly, we developed a recommendation model based on collaborative filtering techniques. Lastly, we integrated our sentiment analysis model into the RS. Our proposed model of SA achieved an accuracy score of 93%, which is superior to other models. The results of our study indicate that our approach enhances the accuracy of the recommendation system. Overall, our proposed system offers customers a more reliable and personalized recommendation service in e-commerce.

Keywords: sentiment analysis; recommender system; deep learning; collaborative filtering; ensemble learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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