Challenges and strategies for sentiment analysis of irony and humor in social media based on machine learning

Abstract

Sentiment analysis of irony and humor in social media poses a formidable challenge owing to the complexity and context-dependency of such expressions. This report offers a comprehensive overview of diverse machine learning techniques utilized in the analysis of irony and humor, encompassing traditional machine learning algorithms and deep learning approaches. Moreover, this review scrutinizes the challenges and future prospects for the sentiment analysis of irony and humor in social media. Future research pathways involve cross-lingual and cross-cultural analysis, multimodal information integration, autonomous identification of novel patterns, adversarial training, and augmenting the explainability and interpretability of sentiment analysis models. The report highlights the importance of these challenges and potential directions, unveiling their impact on this advancing field of research.