Emotion Recognition from Text and Audio Dataset Using Cumulative Attribute Graph Neural Networks (CA-GNN) - Alsaadawi, Hussein Farooq Tayeb; Das, Resul (Springer International Publishing Ag, 2025)
Emotions play a critical role in human understanding and interpersonal communication. Deciphering emotions from text and audio sources presents significant challenges in Affective Computing and Human-Computer Interaction. The challenge intensifies for those with hearing or speech impairments, necessitating accessible technologies. Our study proposes a new approach, employing advanced AI to tackle text and audio data emotion recognition complexities. This approach directly caters to the unique needs of deaf and speech-impaired communities, striving to enhance the accuracy of emotion recognition and understanding. Our study innovates by integrating text and audio features via the CMU-Multimodal Opinion Sentiment and Emotion Intensity CMU-MOSI dataset, employing t-distributed Stochastic Neighbor Embedding (t-SNE) visualizations for comprehensive feature representation. This fusion approach aims to boost emotion detection accuracy by harnessing more information from each modality. Using CA-GNNs for classification, our method leverages the combined feature set for accurate emotion identification, thereby enhancing model effectiveness.
2nd Decision Science Alliance International Summer Conference-DSA ISC -- JUN 06-07, 2024 -- Valencia, SPAIN
Keywords: Bimodal emotion recognition; t-distributed Stochastic Neighbour Embedding (t-SNE); Emotion recognition; cumulative attribute-weighted graph neural network (CA-GNNs)