  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 and 71
percent on the second dataset. Due to the complexity of Persian, it was a
difficult task to achieve this level of efficiency. The main objective of our
research was to reduce the training time while maintaining the model's
performance. As a result, several adjustments were made to the model
architecture and parameters. In addition to achieving the objective, the
performance was slightly improved as well.
