Proposing an approach based on deep learning and sentiment lexicon for Persian sentiment analysis

Articles in Press

Document Type : Original Article

Authors

1 Information Technology Department, Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan

2 Dept. of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan

Abstract

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.

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Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 17 March 2025

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History

  • Receive Date: 24 October 2024
  • Revise Date: 10 March 2025
  • Accept Date: 15 March 2025

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