Context-Aware Models with Rule-Based System Incorporating Historical and Situational Context to Improve the Understanding and Detection of SARCASM

The detection of SARCASM in text presents a significant challenge in natural language processing due to its reliance on contextual subtleties and the interplay between literal and intended meanings. This research aims to develop context-aware models with the rule-based system that incorporate both historical and situational context to enhance the understanding and detection of SARCASM. Propose a multi-faceted approach that integrates linguistic cues, user-specific historical data, and situational information to capture the nuances of sarcastic expressions. The historical context encompasses users' prior interactions and communication patterns, while the situational context involves the immediate conversational environment and external factors influencing the dialogue. The proposed context-aware models are evaluated on benchmark SARCASM detection datasets and real-world social media data to assess their effectiveness and robustness. This research contributes to the broader sentiment analysis and conversational AI field, offering potential applications in social media monitoring, customer service automation, and human-computer interaction.