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David Cheung

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2 papers

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AAAI Conference 2015 Conference Paper

Improving Microblog Retrieval from Exterior Corpus by Automatically Constructing Microblogging Corpus

Wenting Tu
David Cheung
Nikos Mamoulis

A large-scale training corpus consisting of microblogs belonging to a desired category is important for highaccuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and laborconsuming. Therefore, some models for the automatic retrieval of microblogs from an exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblogging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.

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AAAI Conference 2015 Conference Paper

Time-Sensitive Opinion Mining for Prediction

Wenting Tu
David Cheung
Nikos Mamoulis

Users commonly use Web 2. 0 platforms to post their opinions and their predictions about future events (e. g. , the movement of a stock). Therefore, opinion mining can be used as a tool for predicting future events. Previous work on opinion mining extracts from the text only the polarity of opinions as sentiment indicators. We observe that a typical opinion post also contains temporal references which can improve prediction. This short paper presents our preliminary work on extracting reference time tags and integrating them into an opinion mining model, in order to improve the accuracy of future event prediction. We conduct an experimental evaluation using a collection of microblogs posted by investors to demonstrate the effectiveness of our approach.

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