Co-training over Domain-independent and Domain-dependent Features for Sentiment Analysis of an Online Cancer Support Community

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Paper on co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community.

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5 p.

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Biyani, Prakhar; Caragea, Cornelia; Mitra, Prasenjit; Zhou, Chong; Yen, John; Portier, Kenneth et al. August 2013.

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The UNT College of Engineering promotes intellectual and scholarly pursuits in the areas of computer science and engineering, preparing innovative leaders in a variety of disciplines. The UNT College of Engineering encourages faculty and students to pursue interdisciplinary research among numerous subjects of study including databases, numerical analysis, game programming, and computer systems architecture.

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Paper on co-training over domain-independent and domain-dependent features for sentiment analysis of an online cancer support community.

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5 p.

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© IEEE | ACM 2013. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record is published in the ASONAM 2013 proceedings.

Abstract: Sentiment analysis has been widely researched in the domain of online review sites with the aim of getting summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users in online health communities such as cancer support forums, etc. Online health communities act as a medium through which people share their health concerns with fellow members of the community and get social support. Identifying sentiments expressed by members in a health community can be helpful in understanding dynamics of the community such as dominant health issues, emotional impacts of interactions on members, etc. In this work, we perform sentiment classification of user posts in an online cancer support community (Cancer Survivors Network). We use Domain-dependent and Domain-independent sentiment features as the two complementary views of a post and use them for post classification in a semi-supervised setting using the co-training algorithm. Experimental results demonstrate effectiveness of our methods.

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  • Institute of Electrical and Electronics Engineers (IEEE)/Association for Computing Machinery (ACM) International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2013, Niagara Falls, Canada

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  • August 2013

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  • Sept. 20, 2013, 3:37 p.m.

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  • March 27, 2014, 11:38 a.m.

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Biyani, Prakhar; Caragea, Cornelia; Mitra, Prasenjit; Zhou, Chong; Yen, John; Portier, Kenneth et al. Co-training over Domain-independent and Domain-dependent Features for Sentiment Analysis of an Online Cancer Support Community, paper, August 2013; [New York, New York]. (digital.library.unt.edu/ark:/67531/metadc181698/: accessed August 22, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.