Advanced Data Mining and Applications: 9th International by Lishan Cui, Xiuzhen Zhang, Yan Wang, Lifang Wu (auth.),

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 foreign convention on complicated information Mining and purposes, ADMA 2013, held in Hangzhou, China, in December 2013.
The 32 ordinary papers and sixty four brief papers awarded in those volumes have been conscientiously reviewed and chosen from 222 submissions. The papers integrated in those volumes disguise the next subject matters: opinion mining, habit mining, facts circulate mining, sequential facts mining, net mining, photo mining, textual content mining, social community mining, category, clustering, organization rule mining, development mining, regression, predication, characteristic extraction, identity, privateness upkeep, functions, 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

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This paper focuses on improving the POS-pattern match methodology. The core idea of our approach is picking out the effective comment sentences before feature and sentiment extraction based on neural network training. Three attributes of sentences are selected to learn the classification algorithm. Experiment gives the superior parameters of the algorithm. We report the classification performance and also compare the feature extraction performance with classification process and not. The result on practical data set demonstrates the effect of this approach.

20 4 Z. Salah, F. Coenen, and D. Grossi Opinion Mining Once we have generated a sentiment lexicon as described above we can apply these for the purpose of opinion (sentiment) mining. Given a new text which we wish to classify as expressing either a positive or a negative opinion we first apply POST so that each term in the new document is paired with a POS tag. We then lookup each term in the new text in our lexicon taking account of its associated POS tag. 0) for term ti in the new text (speech) obtained from a lexicon, z is the number of terms in the new text and ω is the occurrence count for term ti in the new text.

Both techniques use labelled corpora (training data) from a specific domain. An example of the first technique (creating a new dedicated lexicon) can be found in [10] who proposed a semiautomated mechanism to extract domain-specific health and tourism words from noisy text so as to create a domain-specific lexicon. Example of the second technique (adapting an existing general lexicon) can be found in [11] and [12]. In [11] a simple algorithm is proposed to adapt a generic sentiment lexicon to a specific domain by investigating how the words from the generic lexicon are used in a specific domain in order to assign new polarities for those words.

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