# Enhancing Collaborative Filtering-Based Recommender System Using Sentiment Analysis
## 1 Introduction  
The abundance of information available on the internet has created a challenge for users in sorting through and making decisions among the many options available for services such as restaurants, products, and hotels. This issue, known as information overload, can complicate the decision-making process [1]. To address this problem, recommendation systems have been developed to filter information and provide customized recommendations to users based on their specific tastes and preferences [2]. The goal of these systems is to minimize the time users spend searching for information and suggest items they may not have otherwise considered, thereby improving the quality of information access services. The evolution of e-commerce websites has emphasized the value of recommendation systems in helping customers discover products that are relevant to their needs and preferences. RS has proven to be a useful tool in this context [3]. Several techniques are used for deciding which items to recommend in RSs, with the three most common techniques being collaborative filtering (CF), content-based filtering(CBF), and hybrid methods combining both [4]. CF is widely used and can be found on the majority of online shopping sites [5]. CF systems provide recommendations to a particular user based on the preferences and tastes of other users. These systems can be divided into two types: memory-based (MRB) and model-based (MB). The MRB method utilizes the similarity between items or users to retrieve information for the target user and make recommendations based on the obtained results [6]. The MB method builds a model to predict the ratings or preferences of the target user for a certain item and makes recommendations based on the estimated ratings. It includes two techniques: user-based and item-based [7]. In contrast, the CBF approach compares the semantic content of items [8]. The hybrid technique combines two or more recommendation algorithms or components into an RS [9]. However, traditional RS methods primarily depend on a single standard rating (overall score) for the recommendation process, which is usually insufficient for precise recommendations, as the overall score cannot provide a detailed analysis of the user's behavior [10]. Furthermore, CF faces two main problems, sparsity and gray sheep [11], which make this method unreliable in some recommender systems. This motivates further research aimed at discovering practical solutions to improve the effectiveness of RS. Recently, customer reviews have had a significant impact on customers' decisions to use a service or purchase a product. Many consumers rely on the opinions of others when making decisions, leading to a substantial rise in the number of online customer reviews. Each review reflects the customer's experience with a particular service, such as watching a movie, buying a product, or booking a room. In this regard, Sentiment Analysis methods can be utilized to deduce the customer's emotions and opinions on various topics [12]. The objective of SA is to determine the emotional tone or attitude expressed in user-generated text related to a specific topic or entity [13]. This is achieved by automatically identifying and extracting information about the discussed entity and assessing whether the language used in the text conveys a negative, positive, or neutral sentiment. SA can be performed at three levels of data extraction [14]: aspect, sentence, and document level. There are three main approaches for solving the SA problem [15]: Lexicon-based methods, Machine Learning-based methods, and Hybrid methods. Lexicon-based methods were the first to be employed for SA. They rely on lexicons and linguistic rules and can be classified into two types: corpus-based and dictionary-based [16]. Machine Learning (ML)-based techniques include traditional and deep learning (DL) methods [17]. Finally, a hybrid approach combines lexicons and machine learning techniques [13].