University of Kashan Soft Computing Journal 2322-3707 14 2 2026 01 21 Proposing an approach based on deep learning and sentiment lexicon for Persian sentiment analysis Proposing an approach based on deep learning and sentiment lexicon for Persian sentiment analysis 176 189 114874 10.22052/scj.2025.255682.1278 FA Samira Noferesti Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran. 0000-0002-3751-916X Mahshid Miri Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran. Journal Article 2024 10 24 Sentiment analysis is one of the important branches of natural language processing, which aims to classify texts with respect to the feelings and attitudes of the author of the text. In Persian, most of the available sentiment lexicons are small in size and lack slang expressions and informal words. These features significantly reduce the performance of sentiment analysis algorithms. This paper aims to present a method based on deep learning and sentiment lexicons for sentiment analysis of Persian texts written on social networks. Since most existing sentiment lexicons in Persian language are small in size and lack slang and informal expressions, first, two methods based on ChatGPT are proposed to expand the existing Persian sentiment lexicons by adding slang expressions that are widely used in social media. Then, the combination of the sentiment lexicon and dual-channel convolutional neural network (DC-CNN) is used to determine the polarity of texts. Experimental results show that by expanding the existing sentiment lexicons with the two proposed methods, the accuracy of the sentiment analysis algorithm increases by 1.74 and 2.14 percent, respectively, which indicates the success of ChatGPT in polarity classification of Persian slang expressions. Also, employing the features extracted from the sentiment lexicon in a DC-CNN leads to an increase in the precision of the two base models by 1.6 and 3.2 percent. Sentiment analysis is one of the important branches of natural language processing, which aims to classify texts with respect to the feelings and attitudes of the author of the text. In Persian, most of the available sentiment lexicons are small in size and lack slang expressions and informal words. These features significantly reduce the performance of sentiment analysis algorithms. This paper aims to present a method based on deep learning and sentiment lexicons for sentiment analysis of Persian texts written on social networks. Since most existing sentiment lexicons in Persian language are small in size and lack slang and informal expressions, first, two methods based on ChatGPT are proposed to expand the existing Persian sentiment lexicons by adding slang expressions that are widely used in social media. Then, the combination of the sentiment lexicon and dual-channel convolutional neural network (DC-CNN) is used to determine the polarity of texts. Experimental results show that by expanding the existing sentiment lexicons with the two proposed methods, the accuracy of the sentiment analysis algorithm increases by 1.74 and 2.14 percent, respectively, which indicates the success of ChatGPT in polarity classification of Persian slang expressions. Also, employing the features extracted from the sentiment lexicon in a DC-CNN leads to an increase in the precision of the two base models by 1.6 and 3.2 percent. https://scj.kashanu.ac.ir/article_114874_2a1905147b9bc31de56c4fe487c0f72a.pdf