In the realm of ChatGPT's language capabilities, exploring Arabic Sentiment Analysis emerges as a crucial research focus. This study centers on ChatGPT, a popular machine learning model engaging in dialogues with users, garnering attention for its exceptional performance and widespread impact, particularly in the Arab world. The objective is to assess people's opinions about ChatGPT, categorizing them as positive or negative. Despite abundant research in English, there is a notable gap in Arabic studies. We assembled a dataset from X (formerly known as Twitter), comprising 2,247 tweets, classified by Arabic language specialists. Employing various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naïve Bayes (NB), we implemented hyperparameter optimization techniques such as Bayesian optimization, Grid Search, and random search to select the best hyperparameters that contribute to achieving the best performance. Through training and testing, performance enhancements were observed with optimization algorithms. SVM exhibited superior performance, achieving 90% accuracy, 88% precision, 95% recall, and 91% F1 score with Grid Search. These findings contribute valuable insights into ChatGPT's impact in the Arab world, offering a comprehensive understanding of sentiment analysis through machine learning methodologies.

在 ChatGPT 的语言能力领域，探索阿拉伯语情感分析成为一个重要的研究焦点。这项研究以 ChatGPT 为中心，这是一种流行的机器学习模型，可与用户进行对话，因其卓越的性能和广泛的影响（尤其是在阿拉伯世界）而受到关注。目的是评估人们对 ChatGPT 的看法，将其分为正面或负面。尽管英语研究丰富，但阿拉伯语研究仍存在显着差距。我们从 X（以前称为 Twitter）收集了一个数据集，其中包含 2,247 条推文，由阿拉伯语言专家分类。采用各种机器学习算法，包括支持向量机（SVM）、逻辑回归（LR）、随机森林（RF）和朴素贝叶斯（NB），我们实现了贝叶斯优化、网格搜索和随机搜索等超参数优化技术，选择有助于实现最佳性能的最佳超参数。通过训练和测试，观察到优化算法的性能增强。SVM 表现出卓越的性能，通过网格搜索实现了 90% 的准确率、88% 的精确度、95% 的召回率和 91% 的 F1 分数。这些发现为了解 ChatGPT 在阿拉伯世界的影响提供了宝贵的见解，通过机器学习方法提供了对情感分析的全面理解。