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Machine Learning to Predict Sarcasm in Article headlines

##article.authors##

  • Siddarth Thota Central Bucks South High School

DOI:

https://doi.org/10.58445/rars.1469

Keywords:

Machine Learning, Classification

Abstract

This paper describes a machine learning model designed to assign sets of possible labels to article headlines. This was done by analyzing a dataset with article headlines from 2 sources: The Onion and The Huffpost. The Onion is a site known for generally sarcastic headlines, while the Huffpost has more genuine headlines. The data was then inputted into a Zero-Shot-Classification model, which labeled each headline as either with labels generally pertaining to "fake" or "real”. This accuracy data is subsequently displayed with bar charts.

 

References

Dataset: https://www.kaggle.com/datasets/rmisra/news-headlines-dataset-for-sarcasm-detection/data

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Posted

2024-08-10

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Copyright (c) 2024 Siddarth Thota

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