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.
Top1.Introduction
The study of human emotions has gained considerable focus in the domains of machine learning and artificial intelligence. The capacity to recognise and analyse human emotional states known as emotion modelling has profound effects on a variety of fields, including affective computing, sentiment analysis, and human-computer interaction. Using deep learning methods, especially LSTM (Zhang et al., 2020) networks, to capture the dynamic and temporal aspect of emotional expressions is one well-known strategy in this field. Emotions have a complex and multifaceted pattern that changes over time and are essential to human behaviour and communication (Jiang, Jiao, Wang, Zhang, & Wu, 2021). The temporal connections and delicate transitions seen in emotional states are frequently too nuanced for traditional emotion modelling techniques to accurately represent. The capacity of deep learning to handle sequential data efficiently offers an alluring path towards enhancing the precision and level of detail in emotion modelling (Ma et al., 2019).
The construction and use of LSTM networks for emotion modelling are explored in this research. Recurrent neural networks (RNNs) (Du et al., 2020) of the long-term dependency (LSTM) type were developed especially to tackle the problem of encoding long-term dependencies in sequential input. They can selectively store and recover information over long periods of time thanks to the memory cells and gates included in its architecture. Our goal is to better comprehend human affect by disentangling the intricacies of emotional expressions through the integration of LSTMs into emotion modelling (Zhao et al., 2019).
Developing artificial intelligence systems that are more sensitive and empathic requires an understanding of and modelling of emotions. The user experience in human-computer interaction can be greatly improved by computers' capacity to identify and respond to users' emotional states (Yin et al., 2021). A type of emotion modelling called sentiment analysis is essential for analysing social media sentiment, product evaluations, and public opinion. The foundation of affective computing, an interdisciplinary field that involves creating computers that can perceive, understand, and react to human emotions, is emotion modelling. Conventional methods for modelling emotions, which depend on manually created features and rule-based frameworks, are inadequate for expressing the complexity and nuance of human emotions. With its data-driven methodology, deep learning presents a viable answer by enabling models to automatically derive hierarchical representations from unprocessed data. As an RNN variation, LSTMs are especially well-suited for jobs that need sequential data, which makes them a prime choice for emotion modelling (Pokkuluri & Usha Devi Nedunuri, 2020).
The temporal dynamics of emotional manifestations is one of the main challenges in emotion modelling. Emotions are dynamic; they change throughout time and are frequently impacted by internal or external factors. These temporal connections are difficult for traditional machine learning models to grasp, which results in a loss of important details and subtlety in the depiction of emotions (Xie et al., 2019). LSTMs are a good option for situations where the evolution of emotions is a crucial component because they are specifically developed to solve this problem and perform exceptionally well in modelling long-range dependencies.