Read e-book online Advanced Data Mining and Applications: 8th International PDF

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

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Read Online or Download Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings PDF

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

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The number of hits returned by the Weibo search engine is utilized to estimate the co-occurrence of the candidate word and emoticon. Suppose w represents a sentiment word in the learned lexicon, e is an emoticon with explicit orientation, so we give the definition of the Normalized Weibo Distance (N W D) as follows: N W D(w, e) = max{log f (w), log f (e)} − log f (w, e) log N − min{log f (w), log f (e)} (5) where f (w) denotes the number of hits returned by Weibo search interface using w as keyword; f (e) represents the number of hits returned by Weibo search interface using emoticon e as keyword (Note that we can use escape characters Unsupervised Learning Chinese Sentiment Lexicon 33 to search emoticons in Weibo); f (w, e) denotes the hit number by using w and e as joint keywords; N is the number of microblogs in Weibo system.

In this way, the semantic gap can be reduced by using mid-level features to model different content modalities. 1 Bag-of-Features Model In this paper, scale invariant feature transform (SIFT) features are used to model the image components in a document. There have been many other low-level image features such as HOG (histograms of oriented gradients), LBP (local binary Cross-Modal Information Retrieval 17 pattern). The reason for choosing SIFT is for its effectiveness and stability. SIFT features are invariant to rotation, scaling, translation and small distortions [15].

The microblogs that contain negation words are eliminated. (3) For each remaining microblog m, we remove the stop words. The Chinese words that only have one character are also eliminated, because the orientation of this kind of Chinese words is usually ambiguous or domain-specific. (4) Since not all kinds of the words are good emotion indicators, we need not to reserve all words as candidate sentiment words. Here words with part-ofspeech adjective, verb, noun and adverb are selected for the further detecting steps.

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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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