Emotions play a vital role in human communication, and detecting emotions from text data is a challenging task. The ability to automatically recognize emotions from text has many practical applications, such as in sentiment analysis, social media monitoring, and customer feedback analysis.

In this project, we will discuss the working principle of a text emotion recognition model and its important terminologies. We will also provide a detailed description of the model architecture used and its training process. Finally, we will conclude by evaluating the model using confusion matrix and classification report. Here, in the "emotions" column 0: sad 1: happy

slang.txt in Abbreviations step can be taken from: https://www.kaggle.com/datasets/mansis97/slangs