Unparalleled sarcasm: a framework of parallel deep LSTMs with cross activation functions towards detection and generation of sarcastic statements

Sarcasm is a modest kind of mockingly expressing one’s own thoughts. With the advent social networking communication, new routes sociability have proliferated. It may also be stated that four chariots being socially hilarious nowadays are humour, irony, sarcasm, and wit. clever means encapsulating any intrinsic truth, message, or even satire in humorous way. In this paper, we manually extract features benchmark pop culture sarcasm corpus encompassing sarcastic conversations monologues order to build padding sequences from vector representations’ matrices. We suggest hybrid Parallel Long Short Term Networks, each with its activation classifier. Consecutively it achieves 98.31% accuracy among test cases on open-source English literature. Our approach transcends several previous state-of-the-art works results sophisticated statement generation. probable prospects for producing better refined automated