Emotion Recognition From Text Using Multi-Head Attention-Based Bidirectional Long Short-Term Memory Architecture Using Multi-Level Classification

Abstract

Recognition of emotional information is essential in any form of communication. Growing HCI (human-computer interaction) in recent times indicates the importance of understanding of emotions expressed and becomes crucial for improving the system or the interaction itself. In this research work, textual data for emotion recognition is used. The proposal is made for a neural architecture to resolve not less than eight emotions from textual data sources derived from multiple datasets using google pre-trained word2vec word embeddings and a multi-head attention-based bidirectional LSTM model with a one-vs-all multi-level classification. The emotions targeted in this research are anger, disgust, fear, guilt, joy, sadness, shame, and surprise. Textual data from multiple datasets are ingested such as ISEAR, Go Emotions, and Affect dataset. The results show a significant improvement with the modeling architecture with good improvement in recognizing some emotions.