# GCE: An Audio-Visual Dataset for Group Cohesion and Emotion Analysis
## 1 Introduction
Bottom-up components originate from individuals and their emotions, but top-down components are context-dependent and therefore rely on variables such as the environment and group knowledge. Consequently, group emotions may be described as a shared condition in a group that results from combining these two components [11]. The primary goal of emotional computing is to obtain an accurate categorization of the collective emotions, which is a significant challenge that requires the creation of novel datasets that capture the intricate dynamics of both group cohesion and group emotion. Current limitations, such as the preference for visible emotions over genuine emotions due to limitations in data formatting, emphasize the need for datasets that surpass conventional frameworks. These datasets would facilitate a comprehensive comprehension of the complex interplay between group cohesion and group emotion. The acquisition of extensive datasets is crucial for enhancing the precision and practicality of emotional computing models in real-life situations. In this paper, we present a distinctive audio-visual dataset denoted as the \"Group Cohesion and Emotion\" GCE dataset, designed for a comprehensive analysis of group cohesion and group emotion. The GCE dataset references the data collection method of the VGAF [12] dataset. However, the VGAF dataset has a limitation in that the definitions of the age groups and situations are not specific. Therefore, in this study, when collecting the data, we restrict the group members in the video to adults to control for age effects. In addition, we limit the situation to sitting and having a conversation, focusing on detecting group cohesion and group emotions that are needed in counseling psychology. These video clips have been annotated with each segment assigned group cohesion scores within a range of 1 to 7, and the corresponding group emotional classes categorized as negative, neutral, or positive. The principal contributions of this study are summarized as follows:  
* **Group Cohesion and Emotion Dataset Construction:** We present the Audio-Visual Dataset for Group Cohesion and Emotion Analysis (GCE). When collecting the videos for this dataset, we considered control of the age effect and only sitting conversation situations. In addition, this dataset includes annotations by counseling psychology researchers with seven cohesion levels and three emotions for each video clip. * **Addressing Class Imbalance and Duration Challenges**: The dataset is deliberately designed to incorporate challenging aspects, such as unbalanced class distributions and extended video durations. These challenges are inherent in their classification and regression tasks, where the dominant classes often receive more attention than the minority classes. Additionally, longer recordings encompass various levels of cohesion and emotional states, which can complicate accurate predictive modeling. Therefore, this research encourages the development of models that mitigate the impact of skewed class distributions. * **Multimodal Fusion Model for Group Cohesion and Emotion**: We develop the baseline model for the prediction of group cohesion and group emotion. This involves extracting various features from the audio and visual aspects of the dataset, including facial traits, expressions, video context, static speech characteristics, speech emotions, and spectrograms of the speaker's voice. In addition, FNet module and Multi-Head Attention (MHA) are used to combine the visual and audio features. The baseline model effectively fuses multi-modality, showing a higher performance than single modality. The remainder of this paper is organized as follows. Section 2 describes the previous datasets related to group cohesion and group emotion. Next, we introduce the construction of the Group Cohesion and Emotion Dataset in Section 3, and we implement the details of the baseline model in Section 4. Section 5 presents the baseline performance with the analysis and discussion. Finally, we conclude the study in Section 6.