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Hierarchical Classification in Text Mining for Sentiment Analysis
Jinyan Li1; Simon Fong1; Yan Zhuang1; Richard Khoury2
2014-04-02
Conference Name2014 International Conference on Soft Computing and Machine Intelligence
Source PublicationProceedings - 2014 International Conference on Soft Computing and Machine Intelligence, ISCMI 2014
Pages46-51
Conference Date26-27 Sept. 2014
Conference Place26-27 Sept. 2014
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Sentiment analysis in text mining is known to be a challenging task. Sentiment is subtly reflected by the tone, affective state or emotion of a writer's expression in words. Conventional text mining techniques which are based on keyword frequency counting usually run short of accurately detecting such subjective information implied in the text. In this paper we evaluated several popular classification algorithms, along with three filtering schemes. The filtering schemes progressively shrink the original dataset, with respect to the contextual polarity and frequent terms of a document. In general the proposed approach is coined hierarchical classification. The effects of the approach in different combination of classification algorithms and filtering schemes are discussed over three sets of controversial online news articles where binary and multi-class classifications are applied.

KeywordClassification Sentiment Analysis Text Mining
DOIhttps://doi.org/10.1109/ISCMI.2014.37
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS IDWOS:000380446900011
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, University of Macau Taipa, Macau SAR
2.Department of Software Engineering, Lakehead University, Thunder Bay, Canada
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jinyan Li,Simon Fong,Yan Zhuang,et al. Hierarchical Classification in Text Mining for Sentiment Analysis[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2014:46-51.
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