Deep Learning-based Sentiment Analysis of Olympics Tweets

TL;DR

This paper develops a deep learning-based sentiment analysis model to gauge global audience emotions from Olympics-related tweets, demonstrating high accuracy with BERT.

Contribution

It introduces an advanced DL framework for sentiment analysis of Olympic tweets, improving reliability and accuracy over traditional methods.

Findings

BERT achieved 99.23% accuracy in classifying Olympic sentiments.

Deep learning models outperform Naive Bayes in sentiment classification.

The study enhances understanding of global attitudes towards the Olympics.

Abstract

Sentiment analysis (SA), is an approach of natural language processing (NLP) for determining a text's emotional tone by analyzing subjective information such as views, feelings, and attitudes toward specific topics, products, services, events, or experiences. This study attempts to develop an advanced deep learning (DL) model for SA to understand global audience emotions through tweets in the context of the Olympic Games. The findings represent global attitudes around the Olympics and contribute to advancing the SA models. We have used NLP for tweet pre-processing and sophisticated DL models for arguing with SA, this research enhances the reliability and accuracy of sentiment classification. The study focuses on data selection, preprocessing, visualization, feature extraction, and model building, featuring a baseline Na\"ive Bayes (NB) model and three advanced DL models: Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the BERT model can efficiently classify sentiments related to the Olympics, achieving the highest accuracy of 99.23%.