LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues

The development of an automated system for emotion recognition in conversations (ERC) is beneficial to many conversational AI applications, [Hazarika et al., 2021, Bhat et al., 2021]. The recent language model ChatGPT in the domain of conversational AI has shown the usefulness of an automated system for ERC, [Shahriar and Hayawi, 2023, Zhang et al., 2023]. Such a system can help advance research in many disciplines that include computational linguistics, neuroscience, and psychology, [Canales and Martínez-Barco, 2014, Strapparava and Mihalcea, 2008]. There has been a significant effort to understand the emotions in conversations and develop efficient computational techniques and machine learning classifiers for ERC using the information in conversational dialogues, [Huang et al., 2018, 2019]. For example, [Huang et al., 2018]-assuming that the textual information in a dialogue does not deliver sufficient information-proposed an approach to supply emotion information a priori at training. Subsequently, [Huang et al., 2019] have also utilized the Long Short Term Memory networks (LSTM) architecture hierarchically-as an iterative model-to capture contextual emotional features so that the model can predict the emotions in textual dialogues. Machine learning (ML) is a technique that can help us develop such an automated system to recognize emotions in a conversational dialogue by performing the classification of emotions. For example, [Binali et al., 2010] have adapted emotion theories, based on Ekman's model and the OCC (Ortony/Clore/Collins) model, and developed a support vector machine (SVM) classifier for emotion recognition in a web blog data.