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Wiesław WolnyFollow

Abstract

Twitter is an online social networking service on which users worldwide publish their opinions on a variety of topics, discuss current issues, complain, and express many kinds of emotions. Therefore, Twitter is a rich source of data for opinion mining, sentiment and emotion analysis. This paper focuses on this issue by analysing symbols called emotion tokens, including emotion symbols (e.g. emoticons and emoji ideograms). According to observations, emotion tokens are commonly used in many tweets. They directly express one’s emotions regardless of his/her language, hence they have become a useful signal for sentiment analysis in multilingual tweets. The paper describes the approach to extending existing binary sentiment classification approaches using a multi-way emotions classification.

Recommended Citation

Wolny, W. (2016). Emotion Analysis of Twitter Data That Use Emoticons and Emoji Ideograms. In J. Gołuchowski, M. Pańkowska, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Complexity in Information Systems Development (ISD2016 Proceedings). Katowice, Poland: University of Economics in Katowice. ISBN: 978-83-7875-307-0. http://aisel.aisnet.org/isd2014/proceedings2016/CreativitySupport/5.

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Emotion Analysis of Twitter Data That Use Emoticons and Emoji Ideograms

Twitter is an online social networking service on which users worldwide publish their opinions on a variety of topics, discuss current issues, complain, and express many kinds of emotions. Therefore, Twitter is a rich source of data for opinion mining, sentiment and emotion analysis. This paper focuses on this issue by analysing symbols called emotion tokens, including emotion symbols (e.g. emoticons and emoji ideograms). According to observations, emotion tokens are commonly used in many tweets. They directly express one’s emotions regardless of his/her language, hence they have become a useful signal for sentiment analysis in multilingual tweets. The paper describes the approach to extending existing binary sentiment classification approaches using a multi-way emotions classification.