By Lishan Cui, Xiuzhen Zhang, Yan Wang, Lifang Wu (auth.), Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang (eds.)

ISBN-10: 3642539130

ISBN-13: 9783642539138

ISBN-10: 3642539149

ISBN-13: 9783642539145

The two-volume set LNAI 8346 and 8347 constitutes the completely refereed court cases of the ninth foreign convention on complex information Mining and purposes, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 typical papers and sixty four brief papers awarded in those volumes have been rigorously reviewed and chosen from 222 submissions. The papers integrated in those volumes hide the subsequent subject matters: opinion mining, habit mining, facts move mining, sequential facts mining, internet mining, photo mining, textual content mining, social community mining, class, clustering, organization rule mining, trend mining, regression, predication, characteristic extraction, identity, privateness protection, purposes, and desktop learning.

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Additional resources for Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part I

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Log2 Wi,j = ΔT F − IDF = = tfi,j . log2 tfi,j . 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.

Acknowledgment. This work is partially supported by NSFC under grant number 61370205 and Innovation Program of Shanghai Municipal Education Commission (No. 12ZZ060). Effective Comment Sentence Recognition for Feature-Based Opinion Mining 35 References 1. : Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177. ACM (2004) 2. : Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.

The advantages offered by the proposed ΔTF-IDF scheme are that it can be used to assigning sentiment scores to each term taking into consideration term occurrences in both negative and positive texts. Thus the ΔTF-IDF scheme is used to determine sentiment scores for each term. On completion of step 3 each term in the BOW will comprise an 8-tuple of the form: ti , posti , tfi+ , tfi− , dfi+ , dfi− , si . where si is the sentiment score associated with term i. 4 Lexicon Generation In step 4 the desired domain-specific sentiment lexicon is generated.

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Advanced Data Mining and Applications: 9th International Conference, ADMA 2013, Hangzhou, China, December 14-16, 2013, Proceedings, Part I by Lishan Cui, Xiuzhen Zhang, Yan Wang, Lifang Wu (auth.), Hiroshi Motoda, Zhaohui Wu, Longbing Cao, Osmar Zaiane, Min Yao, Wei Wang (eds.)


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