Sentiment analysis of online news using MALLET
Fong,Simon1; Zhuang,Yan1; Li,Jinyan1; Khoury,Richard2
Source PublicationProceedings - 2013 International Symposium on Computational and Business Intelligence, ISCBI 2013
AbstractThe challenge of sentiment analysis consists in automatically determining whether a text is positive or negative in tone. Part of the difficulty in this task stems from the fact that the same words or sentences can have very different sentimental meaning given their context. In our work, we further focus on news articles, which tend to use a more neutral vocabulary, as opposed to the emotionally charged vocabulary of opinion pieces such as editorials, reviews, and blogs. In this paper, we use MALLET (Machine Learning for Language Toolkit) to implement and train several algorithms for sentiment analysis, and run experiments to compare and contrast them. © 2013 IEEE.
KeywordMALLET Sentiment analysis Text mining
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Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Department of Computer and Information Science, University of Macau,Macao
2.Department of Software Engineering, Lakehead University,Thunder Bay,Canada
First Author AffilicationUniversity of Macau
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GB/T 7714
Fong,Simon,Zhuang,Yan,Li,Jinyan,et al. Sentiment analysis of online news using MALLET[C],2013:301-304.
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