## 1 Introduction  
In the modern communication system, people use different social media platforms (e.g., Facebook, Twitter, Instagram, and Flickr) to express their opinions on various issues and activities of their daily life. In these platforms, users can share visual content with the textual one to communicate with others. It is easier to express emotions intuitively through images [1]. There is a percept that _\"A picture is worth a thousand words\"_. From Figure 1, we can understand how an image can be able to deduce an individual's sentiment without any text. In Figure 0(a), the cat is in a happy mood as it is enjoying the fruit. On the other hand, Figure 0(b) represents the storm forecast. Thus, visual sentiment analysis has become a part of our daily lives [2]. The accurate prediction of users' sentiment by using their uploaded images on social media has become an important research challenge [3, 4]. However, the image data may contain inconsistent data, missing data, or duplicated data which leads to various types of uncertainty (e.g., ignorance, incompleteness, imprecision, ambiguity, and vagueness). These uncertainties can obstruct prediction accuracy. For image classification, deep learning methods are widely used as they can represent the accurate and robust features of images [5, 6]. In addition, to handle different types of uncertainty in image data, the Belief Rule-Based Expert System (BRBES) is more applicable [7, 8]. Since an integrated model performs better than a stand-alone model [9, 10, 11], we propose the integration of a deep learning method with BRBES to improve the prediction accuracy in visual sentiment analysis. This is the key contribution of our research work. As a Deep Learning method can process raw data directly, it is used effectively to solve various classification and regression problems. However, as a data-driven approach, it has the limitation of addressing different types of uncertainty [12]. On the contrary, as a knowledge-driven approach, BRBES can address various types of uncertainty (e.g., ignorance, incompleteness, imprecision, ambiguity, and vagueness) [13]. However, it cannot integrate the associative memory in its inference procedure. For example, we use multiplication and division operators to calculate the activation weight of a rule in the BRBES inference framework. However, these provide incorrect activation values of each rule. To solve this issue, a Deep Neural Network (DNN) model can be used to calculate the rule activation weight by providing more accurate values. Therefore, as an integrated framework of the Deep Learning method within the BRBES inference framework, our proposed system provides the exact value of rule activation weights that results in an accurate prediction of sentiment under uncertainty. BRBES is composed of a set of rules and provides results based on those rules. The rules consist of the antecedent and consequent parts. The antecedent part of a rule is based on the input, and the consequent part contains the output. Generally, there are two types of BRBES: one is Conjunctive BRB [14] and another is Disjunctive BRB [15]. The AND logical operator is utilized to connect each antecedent attribute in the Conjunctive BRB, where the OR logical operator is used in the Disjunctive one. For the AND logical operator, Conjunctive BRB takes more time in computation and constitutes a large number of rules in the rule base. In our experiments, we use the Disjunctive one as it needs less time for computation and has a low number of rules. We explore a total of eight belief rule base for BRBES that takes less computational time. In this paper, we address the following research questions: (1) Why we use the Deep Learning model for visual sentiment analysis? (2) What is the advantage of utilizing BRBES in our proposed system? (3) Why and how we combine Deep Learning with BRBES? We compose the remainder of this paper as follows: Section 2 surveys related work on visual sentiment analysis. Section 3 provides an overview of our proposed BRB-DL approach.