TY - JOUR
T1 - Towards building a social emotion detection system for online news
AU - Lei, Jingsheng
AU - Rao, Yanghui
AU - Li, Qing
AU - Quan, Xiaojun
AU - Wenyin, Liu
PY - 2014/7
Y1 - 2014/7
N2 - Social emotion detection of online users has become an important task for mining public opinions. Social emotion detection aims at predicting the readers' emotions evoked by news articles, tweets, etc. In this article, we focus on building a social emotion detection system for online news. The system is built based on the modules of document selection, Part-of-speech (POS) tagging, and social emotion lexicon generation. Empirical studies are extensively conducted on a large scale real-world collection of news articles. Experiments show that the document selection algorithm has a positive effect on the social emotion detection. The system performs better with the words and POS combination compared to a feature set consisting only of words. POS is also useful to detect emotion ambiguity of words and the context dependence of their sentiment orientations. Furthermore, the proposed method of generating the lexicon outperforms the baselines in terms of social emotion prediction. © 2013 Elsevier B.V. All rights reserved.
AB - Social emotion detection of online users has become an important task for mining public opinions. Social emotion detection aims at predicting the readers' emotions evoked by news articles, tweets, etc. In this article, we focus on building a social emotion detection system for online news. The system is built based on the modules of document selection, Part-of-speech (POS) tagging, and social emotion lexicon generation. Empirical studies are extensively conducted on a large scale real-world collection of news articles. Experiments show that the document selection algorithm has a positive effect on the social emotion detection. The system performs better with the words and POS combination compared to a feature set consisting only of words. POS is also useful to detect emotion ambiguity of words and the context dependence of their sentiment orientations. Furthermore, the proposed method of generating the lexicon outperforms the baselines in terms of social emotion prediction. © 2013 Elsevier B.V. All rights reserved.
KW - Emotion lexicon
KW - Part of speech
KW - Social emotion detection
UR - http://www.scopus.com/inward/record.url?scp=84901593848&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84901593848&origin=recordpage
U2 - 10.1016/j.future.2013.09.024
DO - 10.1016/j.future.2013.09.024
M3 - RGC 21 - Publication in refereed journal
SN - 0167-739X
VL - 37
SP - 438
EP - 448
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -