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Detecting sarcasm in customer tweets: an NLP based approach

Shubhadeep Mukherjee (Department of Information Systems, Indian Institute of Management Ranchi, Ranchi, India)
Pradip Kumar Bala (Information Systems, IIM Ranchi, India)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 10 July 2017

Abstract

Purpose

The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services, etc. This can be immensely helpful in reducing incorrect classification of consumer sentiment toward issues, products and services.

Design/methodology/approach

In this study, 5,000 tweets were downloaded and analyzed. Relevant features were extracted and supervised learning algorithms were applied to identify the best differentiating features between a sarcastic and non-sarcastic sentence.

Findings

The results using two different classification algorithms, namely, Naïve Bayes and maximum entropy show that function words and content words together are most effective in identifying sarcasm in tweets. The most differentiating features between a sarcastic and a non-sarcastic tweet were identified.

Practical implications

Understanding the use of sarcasm in tweets let companies do better sentiment analysis and product recommendations for users. This could help businesses attract new customers and retain the old ones resulting in better customer management.

Originality/value

This paper uses novel features to identify sarcasm in online text which is one of the most challenging problems in natural language processing. To the authors’ knowledge, this is the first study on sarcasm detection from a customer management perspective.

Keywords

Citation

Mukherjee, S. and Bala, P.K. (2017), "Detecting sarcasm in customer tweets: an NLP based approach", Industrial Management & Data Systems, Vol. 117 No. 6, pp. 1109-1126. https://doi.org/10.1108/IMDS-06-2016-0207

Publisher

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Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited