By Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)

ISBN-10: 3642355269

ISBN-13: 9783642355264

ISBN-10: 3642355277

ISBN-13: 9783642355271

This publication constitutes the refereed complaints of the eighth foreign convention on complex information Mining and functions, ADMA 2012, held in Nanjing, China, in December 2012. The 32 typical papers and 32 brief papers provided during this quantity have been rigorously reviewed and chosen from 168 submissions. they're equipped in topical sections named: social media mining; clustering; computing device studying: algorithms and functions; class; prediction, regression and popularity; optimization and approximation; mining time sequence and streaming facts; internet mining and semantic research; info mining purposes; seek and retrieval; details advice and hiding; outlier detection; subject modeling; and information dice computing.

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Additional info for Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings

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Codebook is a way that an image can be represented by a set of local features. The idea is to cluster the feature descriptors of all patches based on a given cluster number and each cluster represents a “visual word” that will be used to form the codebook. After obtaining the codebook, each image can be represented by the BoF histograms of the visual vocabulary of the codebook. The similarity of images can be measured by comparing between the BoF histograms. The Bagof-features model has been well studied as one of the most effective approaches in image classification [5,15].

Bermingham et al. utilized the traditional machine learning based algorithms to classify the microblogs into positive and negative categories [14]. Brody et al. showed that lengthening was strongly associated with subjectivity and sentiment in tweets. 30 S. Feng et al. They proposed several rules to change the lengthening words into their canonical forms and classified the sentiments in Twitter based on the learned words [15]. In [7], 50 Twitter tags and 15 smileys were treated as sentiment labels and a supervised sentiment classification framework was proposed to classify the tweets.

Com has reached 300 million and there are more than 100 million microblog posts everyday. The microblog is a good source for extracting sentiment lexicon. Firstly, as a convenient way to record daily personal feelings and emotions, the microblog data contains rich sentiment information. Secondly, the users usually like to employ free writing style and talk about up-to-date hot topics, so the new words and new meanings are common in the huge microblog dataset. With the microblog API, it is much easier to collect millions of posts for training.

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Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings by Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)


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