Advanced Data Mining and Applications: 8th International by Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik

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 complicated information Mining and functions, ADMA 2012, held in Nanjing, China, in December 2012. The 32 normal papers and 32 brief papers awarded during this quantity have been conscientiously reviewed and chosen from 168 submissions. they're prepared in topical sections named: social media mining; clustering; desktop studying: algorithms and functions; class; prediction, regression and popularity; optimization and approximation; mining time sequence and streaming facts; net mining and semantic research; information mining purposes; seek and retrieval; details suggestion 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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777–785 (2010) 14. : Classifying Sentiment in Microblogs: Is Brevity an Advantage? In: Proc. of CIKM, pp. 1833–1836 (2010) 15. : Cooooooooooooooollllllllllllll!!!!!!!!!!!!!! Using Word Lengthening to Detect Sentiment in Microblogs. In: Proc. of EMNLP, pp. 562–570 (2011) 16. : The Google Similarity Distance. IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007) 17. : Mining Opinions from the Web: Beyond Relevance Retrieval. Journal of American Society for Information Science and Technology 58(12), 1838–1850 (2007) 18.

Wn , SL}. To learn the sentiment lexicon from the purified training dataset, our basic assumption is that the positive sentiment word has more probability to co-occur with positive emoticons and negative sentiment word has more probability to co-occur with negative emoticons. We utilize the classic Pointwise Mutual Information (P M I) to measure the association between the candidate sentiment words and emoticons [10]. Here we have: P M I(w, P E) = log2 p(w, P E) p(w)p(P E) (1) P M I(w, N E) = log2 p(w, N E) p(w)p(N E) (2) where w denote the words in the purified training set D ; p(P E) is the occurrence probability of the positive emoticons in D , which is estimated by NP E /|D | and NP E is the number of positive emoticons; p(w,P E) is the co-occur probability of w and the positive emoticons, which is estimated by NP E−w /|D |.

Agreed with previous research in [16,17], the character-based topic model has a better performance than word-based model for any given topic number. Along with the increase of topic number from 10 to 1000, the MAP of the the word-based model has a tendency of increasing with tiny fluctuation. As for character-based model, it keeps increasing and the MAP is up to 31%, improved by 5% in average compared with the word-based. One possible reason is that the size of word vocabulary is much larger than the size of character vocabulary.

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