TY - GEN
T1 - Co-training over Domain-independent and Domain-dependent features for sentiment analysis of an online cancer support community
AU - Biyani, Prakhar
AU - Caragea, Cornelia
AU - Mitra, Prasenjit
AU - Zhou, Chong
AU - Yen, John
AU - Greer, Greta E.
AU - Portier, Kenneth
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84893312418&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893312418&partnerID=8YFLogxK
U2 - 10.1145/2492517.2492606
DO - 10.1145/2492517.2492606
M3 - Conference contribution
AN - SCOPUS:84893312418
SN - 9781450322409
T3 - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
SP - 413
EP - 417
BT - Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
PB - Association for Computing Machinery
T2 - 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013
Y2 - 25 August 2013 through 28 August 2013
ER -