By Dell Zhang, Karl Prior, Mark Levene, Robert Mao, Diederik van Liere (auth.), Shuigeng Zhou, Songmao Zhang, George Karypis (eds.)
This ebook constitutes the refereed court cases of the eighth foreign convention on complicated facts Mining and purposes, ADMA 2012, held in Nanjing, China, in December 2012. The 32 commonplace 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; laptop studying: algorithms and purposes; type; prediction, regression and popularity; optimization and approximation; mining time sequence and streaming info; internet mining and semantic research; facts mining purposes; seek and retrieval; info 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
2 Word-Based Topics and Character-Based Topics In text modeling, the bag-of-words (BoW) assumption is also well used. For example, by using topic models such as latent Dirichelet allocation (LDA) , a text is represented by a mixture of latent topics, and each topic is represented by a probability distribution over vocabulary. Such word topics can be used to model text components in a document  or used in other natural language processing applications such as Q&A . Most research on topic models only concern English language.
NP E−w 32 S. Feng et al. is the number of microblogs in D that contain both w and positive emoticons. Therefore, the sentiment weight SW of w can be measured by: SW (w) = P M I(w, P E) − P M I(w, N E) (3) Based on Formula 3, we can traverse the puriﬁed microblog dataset and rank the candidate sentiment word by their sentiment weight SW . A threshold θ is deﬁned to control the size of the lexicon as follow. 5 million data are used for sentiment word learning. The details of the experiment setup will be discussed in the Section 5.
For Fig. 4, the given image on the top left is a picture of train and railway. The top three outputs of the TCM are texts about airport, railway system, and airport hotel which are all semantically close to travel and train. For Fig. 5, the system is given a text describe nepenthe (the corresponding image is also shown at below). The top three outputs are all nepenthe images just in diﬀerent kinds. 1 100 200 300 400 500 600 700 800 topic number (word−based) 900 1000 100 200 300 400 500 600 700 800 900 1000 topic number (character−based) Fig.