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 lawsuits of the ninth overseas convention on complicated info Mining and functions, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 general papers and sixty four brief papers provided in those volumes have been conscientiously reviewed and chosen from 222 submissions. The papers incorporated in those volumes conceal the next issues: opinion mining, habit mining, info move mining, sequential info mining, net mining, picture mining, textual content mining, social community mining, class, clustering, organization rule mining, development mining, regression, predication, characteristic extraction, identity, privateness protection, functions, and computer learning.
Read Online or Download Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part I PDF
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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
In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1195–1204. ACM (2009) 5. : Comparison of Model-Based Learning Methods for Feature-Level Opinion Mining. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, WI-IAT 2011, vol. 01, pp. 265–273 (2011) 6. : Advances in Machine Learning Based Text Categorization. Journal of Software, 1848–1859 (2006) (in Chinese) 7. : Multi-facet Rating of Product Reviews.
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proc. ACL (2004) 19. : Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1-2), 1–135 (2008) 20. : An algorithm for suﬃx stripping. Program: Electronic Library and Information Systems 14(3) (1980) 21. : Reputation Systems: Facilitating Trust in Internet Interactions. Communications of the ACM 43, 45–48 (2000) 22. : Sentiment strength detection in short informal text. Journal of the American Society for information Science 61(12), 2544–2558 (2010) 23.
Log2 N + dfi− dfi+ N − N+ dfi+ − tfi,j . log2 N− dfi− (2) Generating Domain-Speciﬁc Sentiment Lexicons for Opinion Mining 19 where: (i) N + is the number of positive texts in the input document collection D (labelled “aye” with respect to our political opinion mining application), (ii) N − is the number of negative texts (labelled “nay”), (iii) tfij is the term frequency for term ti in text j, (iv) dfi+ is the document frequency for term ti with respect to positive texts in the input document collect D and (v) dfi− is the document frequency for term ti with respect to negative texts.
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