{"type":"ScholarlyArticle","title":"Microblog Sentiment Classification via Recurrent Random Walk Network Learning","authors":[{"name":"Zhou Zhao","slug":"zhou-zhao","given":"Zhou","family":"Zhao","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Hanqing Lu","slug":"hanqing-lu","given":"Hanqing","family":"Lu","affiliation":"College of Computer Science , Zhejiang University"},{"name":"Deng Cai","slug":"deng-cai","given":"Deng","family":"Cai","affiliation":"State Key Lab of CAD&CG , Zhejiang University"},{"name":"Xiaofei He","slug":"xiaofei-he","given":"Xiaofei","family":"He","affiliation":"State Key Lab of CAD&CG , Zhejiang University"},{"name":"Yueting Zhuang","slug":"yueting-zhuang","given":"Yueting","family":"Zhuang","affiliation":"College of Computer Science , Zhejiang University"}],"abstract":"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 propose a novel recurrent random walk network learning framework for the problem by exploiting both users’ posted tweets and their social relations in microblogs. We then introduce the deep recurrent neural networks with random-walk layer for heterogeneous MSC network embedding, which can be trained end-to-end from the scratch. Weemploytheback-propagationmethodfortraining the proposed recurrent random walk network model. The extensive experiments on the large-scale public datasets from Twitter show that our method achieves better performance than other state-of-the-art solutions to the problem.","doi":"10.24963/ijcai.2017/494","url":"https://ijcai.org/proceedings/2017/494","pdf":"https://ijcai.org/proceedings/2017/0494.pdf","markdown":"https://ijcai.org/proceedings/2017/494.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/494","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":494,"pages":{"first":3532,"last":3538},"keywords":["Natural Language Processing: Text Classification","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Chang et al., 2015] Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C Aggarwal, and Thomas S Huang. 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