By Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)
This e-book constitutes the refereed lawsuits of the eighth foreign convention on complex info Mining and purposes, ADMA 2012, held in Nanjing, China, in December 2012. The 32 ordinary papers and 32 brief papers offered during this quantity have been conscientiously reviewed and chosen from 168 submissions. they're prepared in topical sections named: social media mining; clustering; computer studying: algorithms and purposes; class; prediction, regression and popularity; optimization and approximation; mining time sequence and streaming facts; net mining and semantic research; information mining functions; seek and retrieval; details advice and hiding; outlier detection; subject modeling; and information dice computing.
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Extra info for Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings
Example text
In: Proc. of KDD, pp. 168–177 (2004) 9. : PageRanking WordNet Synsets: An Application to Opinion Mining. In: Proc. of ACL, pp. 424–431 (2007) 10. : Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: Proc. of ACL, pp. 417–424 (2002) 11. : Fully Automatic Lexicon Expansion for Domainoriented Sentiment Analysis. In: Proc. of ENMLP, pp. 355–363 (2006) 12. : Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents. In: Proc. of EMNLP-CoNLL, pp.
For dataset annotation, we design a microblog sentiment tagging system. Firstly we load the microblog data to the annotation system, which has four basic tags: positive, neutral, negative, and unrelated. After the annotation, the unrelated, spam and advertisement microblogs are eliminated. Three graduate students who major in opinion mining annotate the tags of the microblogs. The final tag result is taken based on the majority opinions of the three students. If there is a disagreement between them, the item is eliminated.
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.