Volume 16, Issue 3 (7-2024)                   itrc 2024, 16(3): 19-24 | Back to browse issues page

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Heydari H, Yazdani A, Farhoodi M. An Improvement in Transformer-Based Sentiment Analysis in Persian Twitter. itrc 2024; 16 (3) :19-24
URL: http://journal.itrc.ac.ir/article-1-652-en.html
An Improvement in Transformer-Based Sentiment Analysis in Persian Twitter
Hadi Heydari1 , Ali Yazdani2 , Mojgan Farhoodi3
1- Faculty of Engineering Kharazmi University Tehran, Iran
2- Faculty of Computer Science and Engineering Shahid Beheshti University Tehran, Iran
3- ICT Research Institute, Tehran, Iran,
Abstract:   (841 Views)
In the era of deep learning, transformer-based models have revolutionized natural language processing tasks, offering unparalleled performance in capturing contextual relationships. This paper delves into the realm of sentiment analysis in Persian Twitter, employing state-of-the-art transformer architectures. Through rigorous experimentation on a dedicated Persian sentiment dataset, we explore the capabilities of transformers in deciphering nuanced emotions expressed in tweets. The results demonstrate the potency of these models, highlighting their effectiveness in understanding the intricacies of sentiment within the Persian language. This study not only contributes insights into sentiment analysis but also underscores the transformative impact of transformer architectures in unlocking the expressive dynamics of Persian social media discourse. We trained multiple deep learning architectures based on transformers for sentiment analysis on Persian Twitter data, and in the test section, we achieved a 60.37% F-score.
Keywords: Sentiment Analysis, Persian Language, Deep Learning, Transformers, Social Media Sentiment Analysis
Full-Text [PDF 573 kb]   (239 Downloads)    
Type of Study: Research | Subject: Information Technology
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