TY - JOUR
T1 - Fast Supervised Topic Models for Short Text Emotion Detection
AU - Pang, Jianhui
AU - Rao, Yanghui
AU - Xie, Haoran
AU - Wang, Xizhao
AU - Wang, Fu Lee
AU - Wong, Tak Lam
AU - Li, Qing
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - With the development of social network platforms, discussion forums, and question answering websites, a huge number of short messages that typically contain a few words for an individual document are posted by online users. In these short messages, emotions are frequently embedded for communicating opinions, expressing friendship, and promoting influence. It is quite valuable to detect emotions from short messages, but the corresponding task suffers from the sparsity of feature space. In this article, we first generate term groups co-occurring in the same context to enrich the number of features. Then, two basic supervised topic models are proposed to associate emotions with topics accurately. To reduce the time cost of parameter estimation, we further propose an accelerated algorithm for our basic models. Extensive evaluations using three short corpora validate the efficiency and effectiveness of the accelerated models for predicting the emotions of unlabeled documents, in addition to generate the topic-level emotion lexicons.
AB - With the development of social network platforms, discussion forums, and question answering websites, a huge number of short messages that typically contain a few words for an individual document are posted by online users. In these short messages, emotions are frequently embedded for communicating opinions, expressing friendship, and promoting influence. It is quite valuable to detect emotions from short messages, but the corresponding task suffers from the sparsity of feature space. In this article, we first generate term groups co-occurring in the same context to enrich the number of features. Then, two basic supervised topic models are proposed to associate emotions with topics accurately. To reduce the time cost of parameter estimation, we further propose an accelerated algorithm for our basic models. Extensive evaluations using three short corpora validate the efficiency and effectiveness of the accelerated models for predicting the emotions of unlabeled documents, in addition to generate the topic-level emotion lexicons.
KW - Accelerated algorithm
KW - emotion detection
KW - short text analysis
KW - topic model
UR - http://www.scopus.com/inward/record.url?scp=85099731179&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2019.2940520
DO - 10.1109/TCYB.2019.2940520
M3 - Article
C2 - 31567111
AN - SCOPUS:85099731179
SN - 2168-2267
VL - 51
SP - 815
EP - 828
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 2
M1 - 8852720
ER -