Y
Yang Liu
Researcher at Wilfrid Laurier University
Publications - 67
Citations - 2822
Yang Liu is an academic researcher from Wilfrid Laurier University. The author has contributed to research in topics: Sentiment analysis & Markov model. The author has an hindex of 20, co-authored 65 publications receiving 2349 citations. Previous affiliations of Yang Liu include York University & Shandong University.
Papers
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Proceedings ArticleDOI
Automatic detection of rumor on Sina Weibo
TL;DR: This is the first study on rumor analysis and detection on Sina Weibo, China's leading micro-blogging service provider, and examines an extensive set of features that can be extracted from the microblogs, and trains a classifier to automatically detect the rumors from a mixed set of true information and false information.
Proceedings ArticleDOI
ARSA: a sentiment-aware model for predicting sales performance using blogs
TL;DR: ARSA is presented, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance and is compared with alternative models that do not take into account the sentiment Information.
Proceedings ArticleDOI
Modeling and Predicting the Helpfulness of Online Reviews
TL;DR: This paper shows that the helpfulness of a review depends on three important factors: the reviewerpsilas expertise, the writing style of the review, and the timeliness of thereview, and presents a nonlinear regression model for helpfulness prediction.
Journal ArticleDOI
Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain
TL;DR: A case study in the movie domain is conducted, and it is shown that both the sentiments expressed in the reviews and the quality of the reviews have a significant impact on the future sales performance of products in question.
Journal ArticleDOI
Combining integrated sampling with SVM ensembles for learning from imbalanced datasets
TL;DR: It is shown that SVMs may suffer from biased decision boundaries, and that their prediction performance drops dramatically when the data is highly skewed, and an integrated sampling technique is proposed that outperforms individual SVMs as well as several other state-of-the-art classifiers.