A
Aijun An
Researcher at York University
Publications - 193
Citations - 5143
Aijun An is an academic researcher from York University. The author has contributed to research in topics: Association rule learning & Rule induction. The author has an hindex of 33, co-authored 188 publications receiving 4389 citations. Previous affiliations of Aijun An include Keele University & Wuhan University.
Papers
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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.
Proceedings ArticleDOI
Discovering top-k teams of experts with/without a leader in social networks
Mehdi Kargar,Aijun An +1 more
TL;DR: Two procedures that produce top-k teams of experts with or without a leader in polynomial delay are proposed and the effectiveness and scalability of the proposed methods are demonstrated.
Journal ArticleDOI
A roadmap of clustering algorithms: finding a match for a biomedical application
TL;DR: A set of desirable clustering features that are used as evaluation criteria for clustering algorithms are presented and their benefits and drawbacks are outlined as a basis for matching them to biomedical applications.