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.

翻译：近年来，深度学习技术在自然语言处理（NLP）任务中的应用日益受到关注，其中情感分析是最具挑战性的领域之一，尤其在波斯语中。波斯语用户在数千个网站、博客以及社交媒体（如Telegram、Instagram和Twitter）上生成的海量内容，构成了丰富的信息资源。尽管面临诸多挑战，深度学习技术越来越受到青睐，用于从这一庞大的原始数据池中提取有价值的信息。在本研究中，我们引入并实现了一种基于深度学习的混合模型，用于情感分析，使用了来自Digikala在线零售网站的用户评论数据。我们采用了多种深度学习网络和正则化技术作为分类器。最终，我们的混合方法取得了令人瞩目的性能，在正面、负面和中性三个情感类别上的F1得分达到78.3。