Designing LSTM Networks for Emotion Modelling

Abstract

This chapter presents a simple and effective approach to designing LSTM networks for the task of emotion recognition. Emotion modelling plays a crucial role in various applications, such as human-computer interaction, sentiment analysis, and affective computing. The proposed LSTM architecture incorporates sequential information inherent in emotional expressions, allowing the model to capture temporal dependencies and nuances in emotional states. The input data, typically in the form of time-series sequences, is pre-processed to extract relevant features and fed into the LSTM network. The model is trained on labelled emotion datasets, enabling it to learn patterns and relationships between input features and corresponding emotional states. To enhance the network's performance, hyper parameter tuning, and regularization techniques are employed. The model's effectiveness is evaluated on benchmark emotion datasets, demonstrating its capability to accurately predict and classify various emotional states.