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Issue No. 05 - May (2017 vol. 29)
ISSN: 1041-4347
pp: 977-990
Yueting Zhuang , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Hanqi Wang , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Jun Xiao , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Fei Wu , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Yi Yang , Center for Quantum Computation and Intelligent Systems, University of Technology, Sydney, NSW, Australia
Weiming Lu , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Zhongfei Zhang , College of Computer Science and Technology, Zhejiang University, Hangzhou, China
ABSTRACT
Many of the words in a given document either deliver facts (objective) or express opinions ( subjective), respectively, depending on the topics they are involved in. For example, given a bunch of documents, the word “bug” assigned to the topic “order Hemiptera” apparently remarks one object (i.e., one kind of insects), while the same word assigned to the topic “software ” probably conveys a negative opinion. Motivated by the intuitive assumption that different words have varying degrees of discriminative power in delivering the objective sense or the subjective sense with respect to their assigned topics, a model named as discriminatively objective- subjective LDA (dosLDA) is proposed in this paper. The essential idea underlying the proposed dosLDA is that a pair of objective and subjective selection variables are explicitly employed to encode the interplay between topics and discriminative power for the words in documents in a supervised manner. As a result, each document is appropriately represented as “bag-of-discriminative-words” (BoDW). The experiments reported on documents and images demonstrate that dosLDA not only performs competitively over traditional approaches in terms of topic modeling and document classification, but also has the ability to discern the discriminative power of each word in terms of its objective or subjective sense with respect to its assigned topic.
INDEX TERMS
Computational modeling, Visualization, Data models, Analytical models, Vocabulary, Semantics, Bayes methods
CITATION

Y. Zhuang et al., "Bag-of-Discriminative-Words (BoDW) Representation via Topic Modeling," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 5, pp. 977-990, 2017.
doi:10.1109/TKDE.2017.2658571
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