By Lishan Cui, Xiuzhen Zhang, Yan Wang, Lifang Wu (auth.), Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang (eds.)
The two-volume set LNAI 8346 and 8347 constitutes the completely refereed court cases of the ninth overseas convention on complicated facts Mining and functions, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 commonplace papers and sixty four brief papers offered in those volumes have been rigorously reviewed and chosen from 222 submissions. The papers integrated in those volumes disguise the next subject matters: opinion mining, habit mining, facts circulation mining, sequential information mining, net mining, photograph mining, textual content mining, social community mining, category, clustering, organization rule mining, development mining, regression, predication, function extraction, id, privateness protection, purposes, and computer learning.
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Extra resources for Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part I
Yang, and X. Liu Pattern Matching Method Analysis We proposed a probability method in our former work . Firstly we split the reviews in the source data set to sentences by punctuations and get word segmentation and part-of-speech (POS) tags for each. Then the typical sequential POS which called POS-patterns are extracted. We calculate the probability for each pattern identifying a subjective sentence and choose some effective patterns to create the pattern set. With the probability for each pattern, the rules for pattern matching are designed.
In: Proceedings of ASCNT-2011, CDAC, Noida, Indiaz (2011) 11. : Learning domain-speciﬁc polarity lexicons. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), pp. 674–679 (2012) 12. : Adapting a polarity lexicon using integer linear programming for domainspeciﬁc sentiment classiﬁcation. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 590–598 (2009) 13. : Using games with a purpose and bootstrapping to create domain-speciﬁc sentiment lexicons.
The research question is weather we can use the co-occurrence relationship among opinion words to boost sentiment classiﬁcation. Our method is based co-training framework. In order to do semisupervised sentiment classiﬁcation, we need to construct three basic sentiment classiﬁers. We ﬁrstly use uni-grams to train a SVM to get the ﬁrst sentiment classiﬁer f1 . Then, we adopt the extracted opinion words from training instances to train to get another sentiment classiﬁer f2 . The third sentiment classiﬁer f3 uses co-occurrence opinion words of the original extracted opinion words extracted from training and unlabeled data sets.
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