Sarcasm Detection of Emojis Using Machine Learning Algorithm

Sarcasm, often elusive in short tweets, poses a challenge for automated sentiment analysis. Our project melds NLP and machine learning to identify sarcastic tweets. We preprocess text for sarcasm clues like irony, hyperbole, and emojis, then train a classifier to predict sarcasm likelihood. We evaluate by splitting sentences, analyzing positive and negative sentiment words. Typically, sarcastic sentences lean toward negative sentiments in the second half. Detecting sarcasm in tweets is crucial for understanding social media conversations and sentiment trends. By leveraging NLP and machine learning, our method offers a promising solution to unravel the subtleties of sarcasm in digital communication. We aim to enhance the accuracy of sentiment analysis tools and improve their ability to capture the nuances of human expression in online discourse.