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 complaints 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 awarded during this quantity have been rigorously reviewed and chosen from 168 submissions. they're geared up 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; internet mining and semantic research; information mining purposes; seek and retrieval; info advice and hiding; outlier detection; subject modeling; and knowledge 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
Many techniques have been proposed aimed at bridging information in diﬀerent modalities [4,6,8,9,11,14]. It has been showed that multi-modal retrieval systems have made signiﬁcant progress compared to uni-modal approaches [11,12,14]. Previous research in  maps the information in diﬀerent modalities into a higher dimensional semantic space where the similarity is measured. In recent 1 Corresponding author. com S. Zhou, S. Zhang, and G. ): ADMA 2012, LNAI 7713, pp. 15–26, 2012. c Springer-Verlag Berlin Heidelberg 2012 16 Y.
Of LREC, pp. 1320–1326 (2010) 7. : Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. In: Proc. of COLING, pp. 241–249 (2010) 8. : Mining and Summarizing Customer Reviews. 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.
Classiﬁcation Performance. We compare our learned lexicon with two famous Chinese sentiment lexicons HowNet  and NTUSD. Note that there is no sentiment weight for each word in HowNet and NTUSD. Therefore, their weights are set to be 1 and -1 for positive and negative words respectively. The classiﬁcation performances with diﬀerent sentiment lexicon are shown in Table 2 and Table 3. Table 2. 4% Table 3. e. more ambiguous emoticons and the words with any POS are considered during the learning process.
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.)