Emoticon analysis for Chinese social media and e-commerce: The azemo system

This article presents a novel system, AZEmo, which extracts and classifies emoticons from the ever-growing critical Chinese social media and E-commerce. An emoticon is a meta-communicative pictorial representation of facial expressions, which helps to describe the sender's emotional state. To c...

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Bibliographic Details
Main Authors: Chen, H. (Author), Jiang, S. (Author), Xing, C. (Author), Yu, S. (Author), Zhang, Y. (Author), Zhu, H. (Author)
Format: Article
Language:English
Published: Association for Computing Machinery 2019
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02393nam a2200349Ia 4500
001 10.1145-3309707
008 220511s2019 CNT 000 0 und d
020 |a 2158656X (ISSN) 
245 1 0 |a Emoticon analysis for Chinese social media and e-commerce: The azemo system 
260 0 |b Association for Computing Machinery  |c 2019 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1145/3309707 
520 3 |a This article presents a novel system, AZEmo, which extracts and classifies emoticons from the ever-growing critical Chinese social media and E-commerce. An emoticon is a meta-communicative pictorial representation of facial expressions, which helps to describe the sender's emotional state. To complement non-verbal communication, emoticons are frequently used in social media websites. However, limited research has been done to effectively analyze the affects of 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 seven affect categories. The system is based on a kinesics model that divides emoticons into semantic areas (eyes, mouths, etc.), with improvements for adaptation in the Chinese context. Machine-learning methods were developed based on feature vector extraction of emoticons. Empirical tests were conducted to evaluate the effectiveness of the proposed system in extracting and classifying emoticons, based on corpora from a video sharing website and an E-commerce website. Results showed the effectiveness of the system in detecting and extracting emoticons from text and in interpreting the affects conveyed by emoticons. © 2019 Association for Computing Machinery. 
650 0 4 |a Affect analysis 
650 0 4 |a Chinese Internet 
650 0 4 |a Chinese internets 
650 0 4 |a Electronic commerce 
650 0 4 |a Emoticon 
650 0 4 |a Feature vector extraction 
650 0 4 |a Learning systems 
650 0 4 |a Machine learning methods 
650 0 4 |a Non-verbal communications 
650 0 4 |a Pictorial representation 
650 0 4 |a Semantics 
650 0 4 |a Social media 
650 0 4 |a Social networking (online) 
700 1 |a Chen, H.  |e author 
700 1 |a Jiang, S.  |e author 
700 1 |a Xing, C.  |e author 
700 1 |a Yu, S.  |e author 
700 1 |a Zhang, Y.  |e author 
700 1 |a Zhu, H.  |e author 
773 |t ACM Transactions on Management Information Systems