# TER-CA-WGNN: Trimodel Emotion Recognition Using Cumulative Attribute-Weighted Graph Neural Network
###### Abstract  
Affective computing is a multidisciplinary field encompassing artificial intelligence, natural language processing, linguistics, computer science, and social sciences. This field aims to deepen our comprehension and capabilities by deploying inventive algorithms. This article presents a groundbreaking approach, the Cumulative Attribute-Weighted Graph Neural Network, which is innovatively designed to integrate trimodal textual, audio, and visual data from the two multimodal datasets. This method exemplifies its effectiveness in performing comprehensive multimodal sentiment analysis. Our methodology employs vocal inputs to generate speaker embeddings trimodal analysis. Using a weighted graph structure, our model facilitates the efficient integration of these diverse modalities. This approach underscores the interrelated aspects of various emotional indicators. The paper's significant contribution is underscored by its experimental results. Our novel algorithm achieved impressive performance metrics on the CMU-MOSI dataset, with an accuracy of 94% and precision, recall, and F1-scores above 92% for Negative, Neutral, and Positive emotion categories. Similarly, on the IEMOCAP dataset, the algorithm demonstrated its robustness with an overall accuracy of 93%, where exceptionally high precision and recall were noted in the Neutral and Positive categories. These results mark a notable advancement over existing state-of-the-art models, illustrating the potential of our approach in enhancing Sentiment Recognition through the synergistic use of trimodal data. This study's comprehensive analysis and significant results demonstrate the proposed algorithm's effectiveness in nuanced emotional state recognition and pave the way for future advancements in affective computing, emphasizing the value of integrating multimodal data for improved accuracy and robustness.  
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crucial [4]. People operate in a manner that is consistent with their emotional perceptions so that they can respond to environmental stimuli. The ability to comprehend human feelings and behaviors is a capability that is very important for intelligent systems, those used in the fields of medicine, robotics, and surveillance [5]. The first and most important phase in the process of emotion identification is the collection of a wide variety of data formats that represent human emotions. An inherent multi-modality characterizes the manifestation of human emotions to the phenomenon [6]. There are several ways in which a person's emotional state can be communicated, including their voice intonation, pace, facial expressions, word choice, and body language. Based on this, it is reasonable to assume that using a variety of modalities, as opposed to depending on, one can provide more favorable outcomes [7]. The assistant editor who was responsible for reviewing the examination of the article and giving final clearance for publishing was tough to get. Considering that humans are not particularly adept at immediately distinguishing distinct emotional categories in text, audio, or video, it is vital to have evidence that has been accurately labeled [8]. Researchers in emotion recognition have enabled public access to datasets for identifying emotions in both text and images [9]. However, there is a noticeable lack of datasets encompassing all three modalities--audio, text, and video--simultaneously, unlike those that are limited to single or dual modalities. The creation of such comprehensive, trimodal datasets involving video, audio, and text is a complex and costly endeavor [10]. This work aims to evaluate the efficient exploitation of datasets containing varied modal information. The primary objective of this research is to advance the field of Emotion Recognition (ER)by introducing and validating a novel trimodal approach Cumulative Attribute-Weighted Graph Neural Network (CA-WGNN).