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ThinkMind // HUSO 2020, The Sixth International Conference on Human and Social Analytics // View article huso_2020_1_50_80038


Emoji as Sentiment Indicators: An Investigative Case Study in Arabic Text

Authors:
Shatha Ali A. Hakami
Robert Hendley
Phillip Smith

Keywords: Emoji; Social Media; Arabic; NLP; Sentiment Analysis

Abstract:
With the explosion of social media usage, researchers have become interested in understanding and analysing the sentiment of the language used in textual digital communications. One particular feature is the use of emoji. These are pictographs that are used to augment the text. They might represent facial expressions, body language, emotional intentions or other things. Despite the frequency with which they are used, research on the interpretation of emoji in languages other than English, such as Arabic, is still in its infancy. This paper analyses the use of emoji in Arabic social media datasets to build a better understanding of sentiment indicators in textual contents. Seven benchmark Arabic datasets containing emoji were manually and automatically annotated for sentiment value. A quantitative analysis of the results shows that emoji are sometimes used as true/direct sentiment indicators. However, the analysis also reveals that, for some emoji and in some contexts, the role of emoji is more complex. They may not act as sentiment indicators, they may act as modifiers of the sentiment expressed in the text or, in some cases, their role may be context dependent. It is important to understand the role of emoji in order to build sentiment analysis systems that are more accurate and robust.

Pages: 26 to 32

Copyright: Copyright (c) IARIA, 2020

Publication date: October 18, 2020

Published in: conference

ISSN: 2519-8351

ISBN: 978-1-61208-800-6

Location: Porto, Portugal

Dates: from October 18, 2020 to October 22, 2020

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