Sentiment analysis is the process of identifying and categorizing people's emotions or opinions regarding various topics. The analysis of Twitter sentiment has become an increasingly popular topic in recent years. In this paper, we present several machine learning and a deep learning model to analysis sentiment of Persian political tweets. Our analysis was conducted using Bag of Words and ParsBERT for word representation. We applied Gaussian Naive Bayes, Gradient Boosting, Logistic Regression, Decision Trees, Random Forests, as well as a combination of CNN and LSTM to classify the polarities of tweets. The results of this study indicate that deep learning with ParsBERT embedding performs better than machine learning. The CNN-LSTM model had the highest classification accuracy with 89 percent on the first dataset with three classes and 71 percent on the second dataset with seven classes. Due to the complexity of Persian, it was a difficult task to achieve this level of efficiency.

翻译：情感分析是识别和分类人们针对各种话题的情绪或观点的过程。近年来，推特情感分析已成为一个日益热门的研究课题。在本文中，我们提出了多种机器学习模型和一种深度学习模型，用于分析波斯语政治推文的情感。我们的分析采用了词袋模型和ParsBERT进行词表示。我们应用了高斯朴素贝叶斯、梯度提升、逻辑回归、决策树、随机森林，以及CNN和LSTM的组合模型来对推文的极性进行分类。研究结果表明，采用ParsBERT嵌入的深度学习模型表现优于机器学习模型。CNN-LSTM模型在第一个包含三个类别的数据集上取得了89%的最高分类准确率，在第二个包含七个类别的数据集上取得了71%的最高分类准确率。由于波斯语的复杂性，达到这一效率水平是一项艰巨的任务。