Political Sentiment Analysis of Persian Tweets Using CNN-LSTM Model

TL;DR

This paper develops a CNN-LSTM deep learning model for Persian political tweet sentiment analysis, achieving high accuracy and reduced training time compared to traditional machine learning methods.

Contribution

Introduces a CNN-LSTM model with optimized architecture for Persian sentiment analysis, outperforming classical machine learning approaches in accuracy and efficiency.

Findings

CNN-LSTM achieved 89% accuracy on first dataset

Deep learning with ParsBERT outperformed traditional ML models

Model adjustments improved training efficiency and performance

Abstract

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.