Emotion detection using Word2Vec and convolution neural networks

Abstract

Emotion detection from text plays a very critical role in different domains, including customer service, social media analysis, healthcare, financial services, education, human-to-computer interaction, psychology, and many more. Nowadays, deep learning techniques become popular due to their capabilities to capture inherent complex insights and patterns from raw data. In this paper, we have used the Word2Vec embedding approach that takes care of the semantic and contextual understanding of text making it more realistic while detecting emotions. These embeddings act as input to the convolution neural network (CNN) to capture insights using feature maps. The Word2Vec and CNN models applied to the international survey on emotion antecedents and reactions (ISEAR) dataset outperform the models in the literature in terms of accuracy and F1-score as model evaluation metrics. The proposed approach not only obtains high accuracy in emotion detection tasks but also generates interpretable representations that contribute to the understanding of emotions in textual data. These findings carry significant implications for applications in diverse domains, such as social media analysis, market research, clinical assessment and counseling, and tailored recommendation systems.