An In-Depth Exploration of Sarcasm Detection Approaches in Twitter Discourse Through Words to Meaning

Automatic sarcasm detection is one of the fastgrowing areas of computer science. As short-form messaging on social media platforms like Twitter grows in popularity, the need to accurately interpret the intent behind these messages has become more critical than ever. Sarcasm is marked by an incongruity between what the words mean and what they were meant to convey, thereby causing potentially severe misunderstandings and miscommunication when there is either insufficient or lacking contextual information. The presence of emojis has complicated this landscape even further. Emojis add emotional tones to text but can obfuscate the true feelings behind a sarcastic comment. For instance, something may appear positive when placed with an upbeat emoji, but changes meaning entirely. This paper discusses the current techniques in sarcasm detection, such as machine learning models, posting history analysis, and context-based detection methods. The trend is emerging to use deep learning techniques more often since they exploit the benefits of models that are based on learned features rather than being solely handcrafted. This paper shall focus on reflecting on the problems and progresses in detecting sarcasm using a wide consideration of those techniques which would be able to deliver deeper insight into enhanced communication within the digital era.