Sentimental Analysis Based on User Emotions Using Machine Learning

Emotion detection and sentiment analysis algorithms are used in various circumstances, particularly when employing interactive systems, to comprehend the polarity or emotions displayed by individuals. Understanding user emotions is a crucial component of human-computer interaction. More natural relationships would be possible with computers that can recognise emotions. Research in this area has increased dramatically as a result of machine learning's expanding capacity to analyse huge data and extract emotions from it. This research paper examines the use of machine learning algorithms to predict sentiments using a dataset of self-reported information. The models - Naive Bayes classifier and Recurrent Neural Network are investigated for predicting the sentiments of the data and compared in terms of their performance. The results of the experiments identify the most suitable model for predicting the sentiments of the user text.