Evaluating Machine learning and Deep-learning methods in Sentiment Analysis on Persian Telegram Message

Today, the Internet, especially social networks such as Twitter, Facebook, and Telegram, has become a platform for exchanging ideas and sharing user opinions. Sentiment analysis based on user opinions in these networks can help explain and predict social phenomena and find suitable products or services for individuals, companies, and organizations. So far, a lot of research has been done on social media data in English; But limited research has been done for the Persian language. In this paper, a Sentiment analysis system on Persian Telegram data is proposed. For this purpose, several feature extraction methods including Countvectorizer, TF-IDF, and word embedding matrix have been studied to represent textual data numerically. Then, to classify the data, different classical machine learning methods including support vector machine, decision tree, K-nearest neighbor, Naïve Bayes, and logistic regression, the combination of classical methods as well as deep learning methods including deep neural network (DNN), convolutional neural network (CNN), long short-term memory network and bidirectional long short-term memory network has been investigated. Finally, the evaluation and analysis of the results on the data collected from Persian Telegram shows that the best performance has been obtained by word embedding and bidirectional long short-term memory network with an accuracy of 90.67%, precision of 90.01%, recall of 89.54% and F1 of 89.77%.