# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 1 Introduction
The application of Deep Learning methods has been shown to be more effective than conventional approaches in sentiment analysis [18]. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Neural Networks (DNNs) can be utilized for sentiment classification. It is common to integrate sentiment analysis methods in recommendation systems to enhance the quality and performance of recommendations [19]. Integrating SA into RS allows for gathering more information about user feedback and preferences on items, which can improve the effectiveness of the system. In this work, our aim is to address the challenge of improving the accuracy and personalization of recommendations in e-commerce systems. Specifically, we investigate the impact of integrating sentiment analysis with collaborative filtering methods in the e-commerce domain. By formulating and addressing this research problem, we contribute to the advancement of recommendation systems in the context of e-commerce by leveraging bi-LSTM-based sentiment analysis. To address our research problem, we conducted a comparative analysis of various recommendation algorithms. We specifically compared their performances under two scenarios: using only ratings to generate recommendations and incorporating both ratings and sentiments for recommendation. In this study, we propose a novel recommender system that leverages Ensemble learning by combining sentiment analysis and collaborative filtering approaches. In contrast to traditional methods, we propose the utilization of a Bi-LSTM model for sentiment analysis, which represents a significant improvement. The strength of the Bi-LSTM model lies in its ability to capture contextual information and dependencies in sequential data, making it well-suited for sentiment analysis tasks. By integrating sentiment analysis based on Bi-LSTM with the recommendation system, our novel approach aims to improve the accuracy and effectiveness of recommendations. The results of the empirical study, conducted with two e-commerce datasets, demonstrate that the combination of Bi-LSTM-based sentiment analysis and collaborative filtering techniques leads to a significant improvement in the performance of the recommender system. These results have been compared with baseline models, providing a means to evaluate the validity and superiority of the proposed approach. The principal contributions of our work are:  
* We have suggested the use of SA based on the Bi-LSTM model for text data to better understand the opinions, emotions, and attitudes of a user. * We developed and evaluated a novel recommendation system that uses ensemble learning by integrating sentiment analysis of text data with collaborative filtering techniques, which improves the accuracy and personalization of recommendations. * Our proposed system was tested on two real-world datasets and the results showed that it outperforms traditional collaborative filtering techniques in terms of predicting evaluation accuracy. This paper is structured as follows. Section 2 presents an overview of the previous research and methods related to the suggested RS. Section 3 describes the approach and techniques used to develop our recommendation system. Section 4 provides an examination and presentation of the results obtained from the experiments that we have performed. Section 5 discusses the results obtained. Lastly, Section 6 presents the main conclusion and highlights future research directions.