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.

本论文采用词袋和 ParsBERT 进行机器学习和深度学习模型分析波斯语政治推文的情感，结果表明，使用 ParsBERT 嵌入的深度学习模型优于机器学习模型，在第一组具有三个类别的数据集上，CNN-LSTM 模型的分类精确度最高，达到 89％，在第二组具有七个类别的数据集上，分类精度为 71％，由于波斯语的复杂性，此研究的效率相对较低。