Advancements in Sarcasm Detection: A Review of Accurate Models for Short Text Data

This review paper explores advancements in sarcasm detection in short text data, crucial for sentiment analysis in digital communication. It traces the evolution from rule-based and statistical methods to advanced machine learning and deep learning models, particularly transformer-based models like BERT, which excel in understanding nuanced contexts. The paper evaluates these models’ accuracy, efficiency, and real-world applicability, highlighting their use in social media monitoring and automated customer service. It also addresses ethical considerations and emphasizes the need for models that better handle contextual and cultural nuances, along with more diverse datasets. The goal is to enhance digital communication by improving sarcasm detection accuracy and reliability.