Surabhi Gupta

Publication Date

Fall 2023

Degree Type

Master's Project

Degree Name

Master of Science in Data Science (MSDS)

Department

Computer Science

First Advisor

William Andreopoulos

Second Advisor

Katerina Potika

Third Advisor

Navrati Saxena

Keywords

Sentiment Analysis, Large Language Models, Twitter Data, Indian Prime Minister Election, Machine Learning Models, Social Media Analytics, Predictive Analysis

Abstract

This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep learning models LSTM, and BERT as our foundational models and compared them with Large Language Models (LLMs) to perform sentiment classification. This analysis aims to offer a nuanced insight into public opinion trends and their potential impact on the upcoming election.

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