# GCE: An Audio-Visual Dataset for Group Cohesion and Emotion Analysis
## 6 Conclusions  
In this paper, we introduced GCE, a new audio-visual dataset dedicated to the study of group cohesion and emotion prediction. The proposed dataset is comprised of 1029 films extracted from YouTube and divided into 30 s segments. Graduate students from the psychology department carefully annotated these video snippets with three emotional classifications (negative, neutral, and positive) and seven levels of cohesion. The dataset was developed in collaboration with Korean groups for a variety of circumstances (interviews, meetings, debates, etc.) using both visual and auditory inputs. We proposed a model based on multi-feature, multi-task group cohesion and emotion prediction. The visual branch retrieved three representation types for facial traits, facial emotion, and the entire video environment. In addition, three types of audio branch representations were recovered using OpenSMILE, Wav2 Vec 2.0, and spectrogram encoding. The MHA fusion module was used to combine these feature representations, and the output of the fusion was supplied to the fully connected layers to predict both the group cohesion scores and the group emotional probability. We observed that our dataset is promising due to the imbalanced classes, extensive duration, and diverse environments, which encourage academics to contribute enthusiastically to this study. However, the GCE dataset has some restrictions. First, it solely investigates the visual and audio modalities, excluding the textual one; however, representations of the textual modality also play a significant role in conserved emotion identification. In addition, the dataset lacks physiological data that could be utilized to enhance the comprehension of cohesion and analytical techniques. To improve prediction performance, we intend to expand the dataset with text data in future studies. In addition, we will study how the interactions between speakers in conversation may affect the group's efficiency and comprehension.  
**Author Contributions:** All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [PERSON], N.-H.H. and [PERSON]; First draft of the manuscript was written by [PERSON]-H.H.; Supervision and project administration were performed by [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2023-00219107), in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korean government (MSIT), and in part by the MSIT (Ministry of Science and ICT), Korea, under the Innovative Human Resource Development for Local Intellectualization support program (IITP-2023-RS-2022-00156287) supervised by the IITP (Institute for Information and Communications Technology Planning and Evaluation).  
**Institutional Review Board Statement:** Regarding Institutional Review Board approval, since it is not directly collected from human subjects, it is under the exemption from IRB review.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[https://sites.google.com/view/gcedataset](https://sites.google.com/view/gcedataset)]([https://sites.google.com/view/gcedataset](https://sites.google.com/view/gcedataset)).  
**Conflicts of Interest:** The authors declare they have no financial interests.