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.
Recommended Citation
Gupta, Surabhi, "Prediction of 2024 Indian PM Election Results Using Sentiment Analysis on Twitter Data" (2023). Master's Projects. 1332.
DOI: https://doi.org/10.31979/etd.q4ka-fjr3
https://scholarworks.sjsu.edu/etd_projects/1332
