An Improvement in Transformer-Based Sentiment Analysis in Persian Twitter

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 ۶۰.۳۷% F-score.

کلیدواژه ها:

Sentiment Analysis ، Persian Language ، Deep Learning ، Transformers ، Social Media Sentiment Analysis