Date of Award

May 2023

Degree Type

Thesis

Degree Name

Master of Science

Department

Computer Science

First Advisor

Rohit Kate

Committee Members

Susan McRoy, Tian Zhao

Keywords

BERT, Emotion Classification, Emotion Intensity, Machine Learning, Natural Language Processing, Twitter data

Abstract

The task of finding an emotion associated with the text from individuals on a social media platform has become very crucial as it influences the current state of mind of a particular individual in real life. It also helps one to understand social behavior at a given point in time. Microblogging platforms like Twitter serves as a powerful tool for expressing one’s thoughts. Several work have been done in classifying the emotion associated with it. The thesis comprises of a system that first classifies the tweet into one of the four emotions - anger, joy, sadness, and fear with good accuracy. It is also important to understand the intensity of the emotion in determining how strong one’s tweet is. Hence, the second phase of the system is built using regressors that help in predicting the intensity of the emotion in the tweet. Both the classification and intensity prediction systems were evaluated on a competition dataset and the regressors outperformed the best system from the competition.

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