TY - GEN
T1 - Listening to the crowd
T2 - 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
AU - Hu, Yuheng
AU - Wang, Fei
AU - Kambhampati, Subbarao
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Individuals often express their opinions on social media platforms like Twitter and Facebook during public events such as the U.S. Presidential debate and the Oscar awards ceremony. Gleaning insights from these posts is of importance to analyzing the impact of the event. In this work, we consider the problem of identifying the segments and topics of an event that garnered praise or criticism, according to aggregated Twitter responses. We propose a flexible factorization framework, SOCSENT, to learn factors about segments, topics, and sentiments. To regulate the learning process, several constraints based on prior knowledge on sentiment lexicon, sentiment orientations (on a few tweets) as well as tweets alignments to the event are enforced. We implement our approach using simple update rules to get the optimal solution. We evaluate the proposed method both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.
AB - Individuals often express their opinions on social media platforms like Twitter and Facebook during public events such as the U.S. Presidential debate and the Oscar awards ceremony. Gleaning insights from these posts is of importance to analyzing the impact of the event. In this work, we consider the problem of identifying the segments and topics of an event that garnered praise or criticism, according to aggregated Twitter responses. We propose a flexible factorization framework, SOCSENT, to learn factors about segments, topics, and sentiments. To regulate the learning process, several constraints based on prior knowledge on sentiment lexicon, sentiment orientations (on a few tweets) as well as tweets alignments to the event are enforced. We implement our approach using simple update rules to get the optimal solution. We evaluate the proposed method both quantitatively and qualitatively on two large-scale tweet datasets associated with two events from different domains to show that it improves significantly over baseline models.
UR - http://www.scopus.com/inward/record.url?scp=84896062601&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896062601&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84896062601
SN - 9781577356332
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2640
EP - 2646
BT - IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Y2 - 3 August 2013 through 9 August 2013
ER -