J
Jing Jiang
Researcher at Singapore Management University
Publications - 233
Citations - 13435
Jing Jiang is an academic researcher from Singapore Management University. The author has contributed to research in topics: Computer science & Chemistry. The author has an hindex of 45, co-authored 160 publications receiving 11573 citations. Previous affiliations of Jing Jiang include Stanford University & Peking University.
Papers
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Proceedings ArticleDOI
TwitterRank: finding topic-sensitive influential twitterers
TL;DR: Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank, which is proposed to measure the influence of users in Twitter.
Book ChapterDOI
Comparing twitter and traditional media using topic models
TL;DR: This paper empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling, and finds interesting and useful findings for downstream IR or DM applications.
Proceedings Article
Instance Weighting for Domain Adaptation in NLP
Jing Jiang,ChengXiang Zhai +1 more
TL;DR: This paper formally analyze and characterize the domain adaptation problem from a distributional view, and shows that there are two distinct needs for adaptation, corresponding to the different distributions of instances and classification functions in the source and the target domains.
Proceedings ArticleDOI
Adaptive filters for continuous queries over distributed data streams
TL;DR: This work considers an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data, and proposes a new technique for reducing the overhead.
Proceedings ArticleDOI
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)
TL;DR: A probabilistic model based on collaborative filtering and topic modeling is proposed that allows it to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on aPer-Aspect basis.