Abstract
An emoticon is a metacommunicative pictorial representation of facial expressions, which serves to convey information about the sender’s emotional state. To complement non-verbal communication, emoticons are frequently used in Chinese online social media, especially in discussions of health and fitness topics. However, limited research has been done to effectively analyze emoticons in a Chinese context. In this study, we developed an emoticon analysis system to extract emoticons from Chinese text and classify them into one of 7 affect categories. The system is based on a kinesics model which divides emoticons into semantic areas (eyes, mouths, etc.), with an improvement for adaption in the Chinese context. Empirical tests were conducted to evaluate the effectiveness of the proposed system in extracting and classifying emoticons, based on a corpus of more than one million sentences of Chinese health- and fitness-related online messages. Results showed the system to be effective in detecting and extracting emoticons from text, and in interpreting the emotion conveyed by emoticons.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Cao, Z., Ye, J.: Attention Savings and Emoticons Usage in BBS. In: Proceedings of the Fourth International Conference on Computer Sciences and Convergence Information Technology (ICCIT 2009), pp. 416–419. IEEE (November 2009)
Chen, J.: The Construction and Application of Chinese Emotion Word Ontology. Master’s Thesis, Dalian University of Technology, China (2009)
Chiu, K.C.: Explorations in the Effect of Emoticon on Negotiation Process from the Aspect of Communication. Master’s Thesis, Department of Information Management, National Sun Yat-sen University, Taiwan (2007)
Derks, D., Bos, A.E., Grumbkow, J.V.: Emoticons and Social Interaction on the Internet: the Importance of Social Context. Computers in Human Behavior 23(1), 842–849 (2007)
Ekman, P.: Basic Emotions. In: Handbook of Cognition and Emotion, vol. 98, pp. 45–60 (1999)
Face-mark Party, http://www.facemark.jp/facemark.htm
Jia, S., Di, S., Fan, T.: Text Sentiment Analysis Model Based on Emoticons and Emotional Words. Journal of the Hebei Academy of Sciences 30(2), 11–15 (2013)
Kaomoji-café, http://kaomojicafe.jp/
Kaomoji Paradise, http://kaopara.net/
Kaomoji Station, http://kaosute.net/jisyo/kanjou.shtml
Kaomojisyo, http://matsucon.net/material/dic/
Kaomoji-toshokan, http://www.kaomoji.com/kao/text/
Kaomojiya, http://kaomojiya.com/
Nakamura, J., Ikeda, T., Inui, N., Kotani, Y.: Learning Face Marks for Natural Language Dialogue Systems. In: Proceedings of International Conference on Natural Language Processing and Knowledge Engineering, pp. 180–185. IEEE (October 2003)
Poongodi, S., Radha, N.: Classification of User Opinions from Tweets Using Machine Learning Techniques. International Journal of Advanced Research in Computer Science and Software Engineering 3(9) (2013)
Ptaszynski, M., Maciejewski, J., Dybala, P., Rzepka, R., Araki, K.: CAO: A Fully Automatic Emoticon Analysis System Based on Theory of Kinesics. IEEE Transactions on Affective Computing 1(1), 46–59 (2010)
Read, J.: Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (June 2005)
Rezabek, L.L., Cochenour, J.J.: Visual Cues in Computer-Mediated Communication: Supplementing Text with Emoticons. Journal of Visual Literacy 18(2) (1998)
Suzuki, N., Tsuda, K.: Express Emoticons Choice Method for Smooth Communication of E-business. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006, Part II. LNCS (LNAI), vol. 4252, pp. 296–302. Springer, Heidelberg (2006)
Tanaka, Y., Takamura, H., Okumura, M.: Extraction and Classification of Facemarks. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 28–34. ACM (January 2005)
Urabe, Y., Rafal, R., Araki, K.: Emoticon Recommendation for Japanese Computer-Mediated Communication. In: Proceedings of the Seventh International Conference onSemantic Computing (ICSC), pp. 25–31. IEEE (September 2013)
Walther, J.B., D’Addario, K.P.: The Impacts of Emoticons on Message Interpretation in Computer-mediated Communication. Social Science Computer Review 19(3), 324–347 (2001)
Wolf, A.: Emotional Expression Online: Gender Differences in Emoticon Use. CyberPsychology & Behavior 3(5), 827–833 (2000)
Yamada, T., Tsuchiya, S., Kuroiwa, S., Ren, F.: Classification of Facemarks Using N-gram. In: Proceedings of International Conference on Natural Language Processing and Knowledge Engineering, pp. 322–327. IEEE (August 2007)
Yang, C., Lin, K.H., Chen, H.H.: Emotion Classification Using Web Blog Corpora. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, pp. 275–278. IEEE (November 2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yu, S., Zhu, H., Jiang, S., Chen, H. (2014). Emoticon Analysis for Chinese Health and Fitness Topics. In: Zheng, X., Zeng, D., Chen, H., Zhang, Y., Xing, C., Neill, D.B. (eds) Smart Health. ICSH 2014. Lecture Notes in Computer Science, vol 8549. Springer, Cham. https://doi.org/10.1007/978-3-319-08416-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-08416-9_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08415-2
Online ISBN: 978-3-319-08416-9
eBook Packages: Computer ScienceComputer Science (R0)