A Machine Learning Model for the Classification of Human Emotions - Ademola Oyemade, David; Favour Seregbe, Diseimokumor (International Journal of Computer Sciences and Engineering, 2024)
Emotions are expressed as part of ordinary speech. Facial expressions, speaking, utterance, writing, gestures and actions are all examples of how humans convey their emotions. Emotions are visible in a large body of research in the domains of psychology, linguistics, social science and communication.as a result, scientific research in emotion has been explored along multiple dimensions and has drawn research from various fields. This paper proposes a model which automatically learns emotions from texts to address the challenge of emotion recognition, noting that language is a powerful tool for communication. We provide automatic recognition in text form of six primary emotions. The use of microblogging was adopted as a rich source of opinion and emotion data. The text under investigation is made up of data gathered from blogs, which reflect writings with high emotional content and hence are appropriate for the study. The first challenge that comes to mind is to create a corpus that is annotated with emotion-related data. Unlike traditional approaches, which rely mostly on statistical methods, we propose a new method which infers and extracts the causes of emotions by incorporating knowledge and theories from other disciplines, such as sociology. The model incorporates Long Short Term Memory (LSTM) machine learning model capable of correctly predicting and classifying human emotions. The results showed that the model produced a 98 percent training accuracy and 88 percent validation accuracy. This concept can be deployed and used in a variety of corporate domains, including marketing, customer support and even the entertainment industry.