# Multimodal Sentiment Classifier Framework for Different Scene Contexts
## 2 Contextualization and State of the Art  
The affiliation, warmth, friendliness, and dominance between two or more individuals are displayed when relationships are made, returned, or deepened [9]. Opposite, impersonal human-machine interactions impede more extensive communication and complicate the establishment of intimate or reciprocal relationships between people and machines, devices, or interfaces. Within this framework, automated data evaluation systems to determine the conveyed emotion are known as automatic emotion analysis approaches [10] (categorized, e.g., as happiness, sadness, fear, surprise, disgust, anger, or neutral) or sentiment [11; 12] (typically limited to positive, negative, and neutral). Considerable advancements have been achieved in the field of sentiment categorization in recent years. Although sentiments and emotions are distinct entities, it should be noticed that they are interconnected [12; 13], i.e., emotions affect sentiments and sentiments affect emotions. The sentiment may be impacted by a variety of elements including attitudes, opinions, emotions, prior experiences, cultural background, personal views, age, or even gender. It is a mental attitude that is connected to a good, negative, or neutral evaluation or thinking about anything [13]. As an example, color can be considered an important feature for sentiment classification [8; 14; 15; 16]. Typically, images of beaches or oceans predominantly feature blue tones, evoking a sense of calm. In contrast, an image of a forest will highlight green tones, which are associated with harmony. Nevertheless, the meaning of a color can change depending on the context in which it is used; for example, the color red can sometimes represent anger, love, or frustration. Also, different individuals may perceive color differently on an emotional level, just like they do with music. Different authors categorize emotions in a variety of ways and break them down into levels and sublevels [13]. Six fundamental emotions, which are typically accompanied by the neutral emotion, is the classification used by the renowned psychologist [PERSON] [10]. This group of emotions is commonly used for facial emotion classification. Other authors have also put forward different categories. For example, based on biological mechanisms, [PERSON] [17] defined the following eight basic/primary emotions:joy, trust, fear, surprise, sorrow, disgust, anger, and anticipation. [PERSON] created a color wheel, known as [PERSON]'s wheel, to symbolize feelings, with a particular color assigned to each. Emotions in this instance may be categorized according to their intensities and combinations; that is, primary emotions are coupled in various ways to generate secondary and tertiary emotions, which are symbolized by various color tones and hues, for a total of 24 emotions. In summary, [PERSON]'s wheel categorizes emotions into the following two primary sentiments: positive and negative. Joy, trust, anticipation, and surprise are examples of good sentiments; on the other hand, sorrow, contempt, fear, and rage are examples of negative sentiments. As it was discussed before [16], the division of emotions into positive and negative sentiments can be subjective and based on individual and cultural variables. Compared to emotion, which can shift quickly in reaction to shifting circumstances and stimuli, sentiment is often longer and more consistent [18]. [PERSON] et al. [13] provided a summary of sentiment analysis in images. The authors outlined the prospects and difficulties in the field and discussed the main problems. To classify the content of composite comments on social media, a multimodal sentiment analysis (text and image) model was published by [PERSON] and [PERSON] [19]. The three primary components of the technique are an image classifier, a text analyzer, and a method that examines an image's class content, determining the likelihood that it falls into one of the potential classes.