By Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)
This publication constitutes the refereed lawsuits of the eighth overseas convention on complicated information Mining and purposes, ADMA 2012, held in Nanjing, China, in December 2012. The 32 typical papers and 32 brief papers awarded during this quantity have been conscientiously reviewed and chosen from 168 submissions. they're equipped 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 information; net mining and semantic research; information mining purposes; seek and retrieval; info advice and hiding; outlier detection; subject modeling; and knowledge dice computing.
Read or Download Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings PDF
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Additional info for Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings
Since it is usually convenient to access Unsupervised Learning Chinese Sentiment Lexicon 31 the microblog data through API, our key idea is to learn sentiment lexicon from extremely large puriﬁed microblog dataset with emoticons. A parallel crawling system is implemented to collect the raw microblog dataset D through Weibo API. The Weibo platform has provided more than 1,000 predeﬁned diﬀerent emoticons. We manually select the most popular emoticons with obvious sentiment orientation, and group them into two categories.
We test the probabilistic correlation model on Ch-wikipedia dataset for the following tasks: (1) obtaining a ranked list of texts from the training database given a query image, (2) obtaining a ranked list of images from the training database given a query text. The mean average precision (MAP) is applied to measure the performance3 . The word-based and character-based topic models are trained separately . When we do the Chinese character-based topic modeling, we remove the rare characters that appear less than 3 times across the whole corpus and the terms appearing in over 50% of the documents which we consider as stop words .
2011CB302200-G), State Key Program of National Natural Science of China (Grant No. 61033007), National Natural Science Foundation of China (Grant No. 61100026, 60973019), and the Fundamental Research Funds for the Central Universities (N100704001). S. Zhou, S. Zhang, and G. ): ADMA 2012, LNAI 7713, pp. 27–38, 2012. c Springer-Verlag Berlin Heidelberg 2012 28 S. Feng et al. 0 based social media. Emerging Words. A great number of new words are emerging in the online social media every day. 0 texts.