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Isn’t It Ironic, Don’t You Think?

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 13111)

Abstract

Currently, dealing with figurative language, such as irony, is one of the challenging and interesting problems in natural language processing. Because irony is so widespread in user-created content (UCC) such as social media posts, its prevalence makes it technically challenging to determine sentiments or interpret opinions. Investigating irony content is an essential problem which can be extended and leveraged for other tasks such as sentiment and opinion mining. The present work proposes a Hierarchical Attention based Neural network for identifying ironic tweets. Our method exploits the structure and semantic features like POS tagging and sentiment flow shifts. We then present our results on the Task 3 of the Semantic Evaluation 2018 workshop named “Irony Detection in English Tweets” dataset. Furthermore, we perform a comprehensive analysis over the generated results, shedding insights on future research for the irony detection task.

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Notes

  1. 1.

    http://www.nltk.org/howto/sentiment.html.

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Correspondence to Swati Agarwal .

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Reddy, S.M., Agarwal, S. (2021). Isn’t It Ironic, Don’t You Think?. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_43

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  • DOI: https://doi.org/10.1007/978-3-030-92273-3_43

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  • Online ISBN: 978-3-030-92273-3

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