Sarcasm detection on facebook: A supervised learning approach

Abstract

Sarcasm is a common feature of user interaction on social networking sites. Sarcasm differs with typical communication in alignment of literal meaning with intended meaning. Humans can recognize sarcasm from sufficient context information including from the various contents available on SNS. Existing literature mainly uses text data to detect sarcasm; though, a few recent studies propose to use image data. To date, no study has focused on user interaction pattern as a source of context information for detecting sarcasm. In this paper, we present a supervised machine learning based approach focusing on both contents of posts (e.g., text, image) and users' interaction on those posts on Facebook.

Department(s)

Computer Science

Document Type

Conference Proceeding

DOI

https://doi.org/10.1145/3281151.3281154

Keywords

Facebook, Image, Sarcasm, Sentiment, Supervised Learning, Text

Publication Date

10-16-2018

Journal Title

Proceedings of the 20th International Conference on Multimodal Interaction, ICMI 2018

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