TY - GEN
T1 - Multi-scale sentiment classification using canonical correlation analysis on Riemannian manifolds
AU - Dai, Shuanglu
AU - Xu, Xingzhong
AU - Jiang, Bitian
AU - Man, Hong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/18
Y1 - 2017/1/18
N2 - Documents with complex sentiment expressions generally pose great challenges in sentiment analysis. This paper proposes a statistical framework to improve sentiment classification within multiscale sentences or paragraphs. A Set of Sentiment Parts (SSP) is first introduced to express sentiment features in different contexts of varying scales. A statistic combination is then determined by analyzing canonical correlations on Riemannian manifolds. A metric learning method is designed to keep the orthogonality within Riemannian point pairs. The nearest neighbor (NN) method is finally used to classify sentiments of SSP. Promising results on various sentiment analysis data sets demonstrate the effectiveness of the proposed method.
AB - Documents with complex sentiment expressions generally pose great challenges in sentiment analysis. This paper proposes a statistical framework to improve sentiment classification within multiscale sentences or paragraphs. A Set of Sentiment Parts (SSP) is first introduced to express sentiment features in different contexts of varying scales. A statistic combination is then determined by analyzing canonical correlations on Riemannian manifolds. A metric learning method is designed to keep the orthogonality within Riemannian point pairs. The nearest neighbor (NN) method is finally used to classify sentiments of SSP. Promising results on various sentiment analysis data sets demonstrate the effectiveness of the proposed method.
KW - Canonical correlation analysis
KW - Multi-scale sentiment classification
KW - Natural language processing
UR - http://www.scopus.com/inward/record.url?scp=85015182932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015182932&partnerID=8YFLogxK
U2 - 10.1109/ISM.2016.15
DO - 10.1109/ISM.2016.15
M3 - Conference contribution
AN - SCOPUS:85015182932
T3 - Proceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016
SP - 144
EP - 147
BT - Proceedings - 2016 IEEE International Symposium on Multimedia, ISM 2016
T2 - 18th IEEE International Symposium on Multimedia, ISM 2016
Y2 - 11 December 2016 through 13 December 2016
ER -