Deep Learning-based Sentiment Analysis in Persian Language
Mohammad Heydari Mohsen Khazeni Mohammad Ali Soltanshahi
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.