Comparison of Artificial Decision Techniques for Detection of Sarcastic News Headlines

Comparison of Artificial Decision Techniques for Detection of Sarcastic News Headlines

Tarun Jain, Horesh Kumar, Payal Garg, Abhinav Pillai, Aditya Sinha, Vivek Kumar Verma
Copyright: © 2023 |Pages: 12
DOI: 10.4018/IJCBPL.330131
Article PDF Download
Open access articles are freely available for download

Abstract

Newspapers are a rich informational source. A headline of an article sparks an interest in the reader. So, news providing agencies tend to create catchy headlines to attract the reader's attention onto them, and this is how sarcasm manages to find its way into news headlines. Sarcasm employs the use of words that carry opposite meaning with respect to what needs to be conveyed. This leads to the need of developing methods by which we can correctly predict whether a piece of text, or news for that matter, truthfully means what it says or is simply being sarcastic about it. Here, the authors have used a dataset containing 55,329 tuples consisting of news headlines from The Onion and the Huffington Post, which was taken from Kaggle, on which they applied feature extraction techniques such as Count Vectorizer, TF-IDF, Hashing Vectorizer, and Global Vectorizer (GloVe). Then they applied seven classifiers on the obtained dataset. The experimental results showed that the highest accuracies among the ML models were 81.39% for LR model with Count Vectorizer, 79.2% for LR model with TF-IDF Vectorizer, and 78% for SVM model with Count Vectorizer. They also obtained the best accuracy of 90.7% using the Bi-LSTM Deep Learning Model. They have trained the seven models and compared them based on their respective accuracies and F1-Scores.
Article Preview
Top

There were 3 papers which made use of the same dataset as ours in their respective approaches. Onyinye & Afli (2020) used word level, n-gram level and character level TF-IDF embedding combined with Count Vectorizer for their research, concluding that supervised learning techniques fare better than deep learning methods for sarcasm detection. Jariwala (2020) used Part-of-Speech tagging to determine connections between words and utilized a rule-based approach to extract optimal features for their research. Their approach gave better results for SVM classifier compared to standard feature extraction. Pelser & Murrell (n.d.) used a Deep and Dense Neural Network to extract additional intrinsic information from the text for making better predictions for standalone utterances.

Complete Article List

Search this Journal:
Reset
Volume 14: 1 Issue (2024)
Volume 13: 1 Issue (2023)
Volume 12: 4 Issues (2022): 1 Released, 3 Forthcoming
Volume 11: 4 Issues (2021)
Volume 10: 4 Issues (2020)
Volume 9: 4 Issues (2019)
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing