Detecting Emotion from Natural Language Text Using Hybrid and NLP Pre-trained Models

Abstract

In current days emotion analysis has been attracting many users to continue their research towards detecting various emotions from natural text. Almost a lot of study is being done in the area of Artificial Intelligence (AI), which mainly focuses on identifying the human state rather than digging the inner reason behind those state or emotions, like why those states are not recognized properly. There were many failures in the primitive methods for emotion detection because of improper correlation among emotions. Hence this motivated me to design a new model, which can fill the gap between emotion recognition and emotion correlation mining through natural language text from text conversations. In this proposed model, we try to mine emotion correlation from emotion recognition through natural text by using various kinds of features and by applying machine learning and deep learning pre-trained models. The main features are anger, disgust, fear, guilt, joy, sadness, and shame. By conducting various experiments on proposed models by taking a sample dataset collected from the Kaggle website, we try to observe the performance metrics of each and every individual algorithm in order to detect the emotions very accurately from natural language text.