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 complaints of the ninth overseas convention on complicated facts Mining and functions, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 average papers and sixty four brief papers offered in those volumes have been conscientiously reviewed and chosen from 222 submissions. The papers integrated in those volumes hide the next subject matters: opinion mining, habit mining, info circulation mining, sequential information mining, net mining, photo mining, textual content mining, social community mining, type, clustering, organization rule mining, trend mining, regression, predication, characteristic extraction, id, privateness upkeep, functions, and laptop learning.
Read 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
A sentiment score is a numeric value indicating some degree of subjectivity. Sentiment orientation is an indicator of whether a word expresses assent or dissent with respect to some object or concept. Consequently, document polarity can be judged by counting the number of assenting and dissenting words, summating their associated sentiment scores and then calculating the diﬀerence. The result represents the polarity (positive or negative) of the document. 0 “oﬀ-the-shelf” sentiment lexicon .
H. Song, B. Yang, and X. 8 Threshold Fig. 4. F-score with different threshold Table 4. 9% Although some works reported CRF-base method achieved a high precision on their data base, but in the practical application, the data set is filled with too many garbage, the precision drops dramatically. Our approach increases the precision about 15%, however, the recall drops about 7%, because in the ECS recognition process, some ECS have been classified to ULS. In this actual situation, dealer wants to give a report of custom feedback based on the tremendous data from the e-commerce site, the precision is more important than recall here.
461–472. Springer, Heidelberg (2009) 8. : Extraction of Unexpected Sentences: A Sentiment Classification Assessed Approach. J. Intelligent Data Analysis 14, 31–46 (2010) 9. : Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 339–346. Association for Computational Linguistics (2005) 10. : Feature-level sentiment analysis for Chinese product reviews. In: 3rd International Conference on Computer Research and Development, Shanghai, pp.