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

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Abstract

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

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Data availability

The datasets analyzed during the current study in subsequent order are openly available as: 1. MUStARD or Multimodal Sarcasm Detection dataset in the Github repository, https://github.com/soujanyaporia/MUStARD 2. The Comedy of Errors in the Project Gutenberg repository, https://www.gutenberg.org/ebooks/2239 3. Three Men in a Boat in the Project Gutenberg repository, https://www.gutenberg.org/ebooks/308 4. Sarcasm SIGN dataset in the Github repository, https://github.com/lotemp/SarcasmSIGN 5. V2 dataset in the JB School of Engineering NLDS Corpora repository, https://nlds.soe.ucsc.edu/sarcasm2 6. SARC dataset in the Princeton CS repository, https://nlp.cs.princeton.edu/SARC/2.0/main/ 7. The source-code of the project will be made available at:https://github.com/SouravD-Me.

Notes

  1. https://en.wikipedia.org/wiki/TheBigBangTheory.

  2. https://www.theonion.com/.

  3. https://www.huffingtonpost.in/.

  4. https://colab.research.google.com/.

  5. https://www.gutenberg.org/.

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  1. Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Future Institute of Technology, Kolkata, 700154, India

    Sourav Das

  2. Department of Computer Science and Engineering, Indian Institute of Technology, Patna, India

    Soumitra Ghosh & Asif Ekbal

  3. Department of Computer Science and Engineering, RCC Institute of Information Technology, Kolkata, 700015, India

    Anup Kumar Kolya

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Das, S., Ghosh, S., Kolya, A.K. et al. Unparalleled sarcasm: a framework of parallel deep LSTMs with cross activation functions towards detection and generation of sarcastic statements. Lang Resources & Evaluation 57, 765–802 (2023). https://doi.org/10.1007/s10579-022-09622-3

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