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%.

翻译：情感分析是自然语言处理的一种方法，通过分析针对特定主题、产品、服务、事件或经历的观点、感受和态度等主观信息，以确定文本的情感基调。本研究旨在开发一种用于情感分析的先进深度学习模型，通过奥运会背景下的推文来理解全球观众的情感。研究结果反映了围绕奥运会的全球态度，并有助于推进情感分析模型的发展。本研究利用自然语言处理技术进行推文预处理，并采用复杂的深度学习模型进行情感分析论证，从而提升了情感分类的可靠性与准确性。研究重点关注数据选择、预处理、可视化、特征提取和模型构建，包括一个基线朴素贝叶斯模型和三种先进的深度学习模型：卷积神经网络、双向长短期记忆网络以及基于Transformer的双向编码器表示模型。实验结果表明，BERT模型能够有效分类与奥运会相关的情感，取得了99.23%的最高准确率。