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Sarcasm Detection in Newspaper Headlines

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Data Science and Emerging Technologies (DaSET 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 191))

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Abstract

Language is an essential medium for human communication. It allows us to convey information, express our ideas, and give instructions to others. The rise of sarcasm can be attributed to the increasing number of negative comments and expressions posted on social networks such as Twitter, Facebook, and newspapers. Due to the use of positive vocabulary in sarcastic comments, it is hard to detect sarcasm in news reports. Sarcasm is intentionally used in news reports to grab the readers’ attention. Unfortunately, many people find it hard to identify the ironic tone of the headlines and may pass incorrect information. This work focuses on detecting sarcasm in newspaper headlines and investigates the performance of four machine learning algorithms (Logistic Regression, Naive Bayes, decision tree, and Random Forest) and one deep learning model BiLSTM (Bi-directional Long Short-Term Memory) for sarcasm detection in news headlines. We demonstrate that regardless of the machine learning model, the application of vectorization technique, i.e. BoW (Bag of Words) and TF–IDF (Term Frequency–Inverse Document Frequency) has minimal influence on the ability to detect sarcasm in news headlines. We also show that the performance of the three machine learning algorithms (Logistic Regression, Random Forest, and decision tree) remains stable across two tokenization techniques (Unigram or Bigram) except Naive Bayes which secured a higher precision with Unigram analysis. We further found that BiLSTM is the most preferred model for sarcasm detection in news headlines.

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Acknowledgements

The authors thank the UNITAR International University supporting the publication of this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Hertfordshire, Hatfield, UK

    Vishnu Sai Reddy Chilpuri & Muhammad Yaqoob

  2. Department of Computer Science, University of Management and Technology, Lahore, Pakistan

    Saaman Nadeem

  3. School of Information Technology, UNITAR International University, Selangor, Malaysia

    Tahir Mehmood

Authors
  1. Vishnu Sai Reddy Chilpuri
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  2. Saaman Nadeem
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  3. Tahir Mehmood
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  4. Muhammad Yaqoob
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Corresponding authors

Correspondence to Tahir Mehmood or Muhammad Yaqoob .

Editor information

Editors and Affiliations

  1. UNITAR Graduate School, UNITAR International University, Petaling Jaya, Malaysia

    Yap Bee Wah

  2. Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily OBE

  3. University of Tennessee, Knoxville, TN, USA

    Michael W. Berry

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Chilpuri, V.S.R., Nadeem, S., Mehmood, T., Yaqoob, M. (2024). Sarcasm Detection in Newspaper Headlines. In: Bee Wah, Y., Al-Jumeily OBE, D., Berry, M.W. (eds) Data Science and Emerging Technologies. DaSET 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 191. Springer, Singapore. https://doi.org/10.1007/978-981-97-0293-0_18

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