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A qualitative analysis of sarcasm, irony and related #hashtags on Twitter

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journal contribution
posted on 23.10.2020, 11:06 by Martin SykoraMartin Sykora, Suzanne Elayan, Tom JacksonTom Jackson
As the use of automated social media analysis tools surges, concerns over accuracy of analytics have increased. Some tentative evidence suggests that sarcasm alone could account for as much as a 50% drop in accuracy when automatically detecting sentiment. This paper assesses and outlines the prevalence of sarcastic and ironic language within social media posts. Several past studies proposed models for automatic sarcasm and irony detection for sentiment analysis; however, these approaches result in models trained on training data of highly questionable quality, with little qualitative appreciation of the underlying data. To understand the issues and scale of the problem, we are the first to conduct and present results of a focused manual semantic annotation analysis of two datasets of Twitter messages (in total 4,334 tweets), associated with; (i) hashtags commonly employed in automated sarcasm and irony detection approaches, and (ii) tweets relating to 25 distinct events, including, scandals, product releases, cultural events, accidents, terror incidents, etc. We also highlight the contextualised use of multi-word hashtags in the communication of humour, sarcasm and irony, pointing out that many sentiment analysis tools simply fail to recognise such hashtag based expressions. Our findings also offer indicative evidence regarding the quality of training data used for automated machine learning models in sarcasm, irony, and sentiment detection. Worryingly only 15% of tweets labelled as sarcastic were truly sarcastic. We highlight the need for future research studies to rethink their approach to data preparation and a more careful interpretation of sentiment analysis.

History

School

  • Business and Economics

Department

  • Business

Published in

Big Data and Society

Volume

7

Issue

2

Publisher

SAGE Publications (UK and US)

Version

VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by SAGE under the Creative Commons Attribution-NonCommercial 4.0 International Licence (CC BY-NC 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc/4.0/

Acceptance date

21/10/2020

Publication date

2020-12-01

Copyright date

2020

ISSN

2053-9517

Language

en

Depositor

Dr Martin Sykora Deposit date: 21 October 2020