  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%.
