Deep Learning-based Sentiment Analysis in Persian Language
This work addresses sentiment analysis for Persian language users, but it is incremental as it applies existing deep learning techniques to a specific domain.
The study tackled sentiment analysis in Persian language by developing a hybrid deep learning model using customer review data from Digikala, achieving an F1 score of 78.3 across positive, negative, and neutral categories.
Recently, there has been a growing interest in the use of deep learning techniques for tasks in natural language processing (NLP), with sentiment analysis being one of the most challenging areas, particularly in the Persian language. The vast amounts of content generated by Persian users on thousands of websites, blogs, and social networks such as Telegram, Instagram, and Twitter present a rich resource of information. Deep learning techniques have become increasingly favored for extracting insights from this extensive pool of raw data, although they face several challenges. In this study, we introduced and implemented a hybrid deep learning-based model for sentiment analysis, using customer review data from the Digikala Online Retailer website. We employed a variety of deep learning networks and regularization techniques as classifiers. Ultimately, our hybrid approach yielded an impressive performance, achieving an F1 score of 78.3 across three sentiment categories: positive, negative, and neutral.