Analysis of Sentiment and Sarcasm in Arabic YouTube Comments Utilizing AraBERT

The emergence of social networks has transformed how people interact, leading to the development of extensive collections of user-generated content that can be examined to gain insights into cultural, social, and linguistic trends. The Arabic language, recognized for its intricate morphological features and various dialects, presents distinctive challenges within the natural language processing domain (NLP). This research explores the intricacies involved in analyzing sentiment and sarcasm within Arabic comments on YouTube by utilizing a labeled dataset that comprises both Modern Standard Arabic (MSA) and colloquial dialects. The study emphasizes the preprocessing stage, which involves addressing missing data, eliminating duplicates, and tokenizing text. It also incorporates feature extraction through AraBERT, a pre-trained transformer model specifically designed for Arabic. By Using sophisticated machine learning techniques, this research seeks to accurately classify sentiment and sarcasm while examining the linguistic patterns that define digital discourse in Arabic. Ultimately, this study aspires to enhance the overall comprehension of how sentiment and sarcasm manifest and are interpreted in Arabic online communications, providing valuable insights into both linguistic subtleties and computational approaches in Arabic NLP.