Sarcasm Detection of Newspaper Headlines Using LSTM-RNN

Even for humans, it can be difficult to recognize sarcasm, which is an important part of communication. To attract readers’ attention, sarcasm is frequently used in newspaper headlines. Although headlines usually include irony, readers frequently miss it, misinterpreting the news and spreading misinformation to friends, coworkers, and other people. As a result, it is more important than ever to have a system that can automatically and reliably recognize sarcasm. In order to build sarcasm detectors, employ neural networks, and investigate how a computer may learn sarcastic patterns. The sequences that are fed into our project might be ironic or not. From collections of news headlines, these sequences were created. Classifiers are well evaluated for accuracy. Our approach is effective at identifying the difference between remarks that are sarcastic and those that are not. Sarcasm identification is a method for spotting expressions that mean the exact opposite of what they mean to say. Systems for sentiment analysis that rely on emotion recognition face a substantial barrier due to the metaphorical character of sarcasm. Natural language processing (NLP) is a highly specialised field that focuses on sarcasm identification rather than sentiment analysis across many domains. Sarcasm detection in online forums is one such application