SVM-based Sarcasm Detection System: NLP Using Heuristic Approach

To create a system that will detect and interpret sarcastic messages in blog and tweet using natural language processing (NLP) technology and support vector machines (SVM). The data used for training and evaluation are available on global blog profiles, making it useful for capturing the nuances of informal conversations. Using NLP, the system understands the context and speech patterns that indicate malicious language, allowing for more accurate detection. Using SVM as a classification technique helps build a robust and reliable model of sarcasm by enabling the system to discern between tweets that are sarcastic and those that aren't. With sarcasm increasingly used in online communication, developing this technique can help improve thinking and understanding of tone and meaning on social media. In the age of thinking, different analytics have emerged that analyze people's behavior and thoughts on social media (such as Facebook, Twitter, blogs and other social networking sites). The main purpose of sentiment analysis is to identify the text's polarity—positive, negative, or neutral.