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Microblog Sentiment Classification via Recurrent Random Walk Network Learning
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Microblog Sentiment Classification (MSC) is a challenging task in microblog mining, arising in many applications such as stock price prediction and crisis management. Currently, most of the existing approaches learn the user sentiment model from their posted tweets in microblogs, which suffer from the insufficiency of discriminative tweet representation. In this paper, we consider the problem of microblog sentiment classification from the viewpoint of heterogeneous MSC network embedding. We
doi:10.24963/ijcai.2017/494
dblp:conf/ijcai/ZhaoLCHZ17
fatcat:qmdeox7u5vgpnmbfmdylfcpcvq