Multi-scale Sentiment Classification Using Canonical Correlation Analysis on Riemannian Manifolds

Shuanglu Dai, Xingzhong Xu, Bitian Jiang, Hong Man

Abstract: 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.

External IDs:dblp:conf/ism/DaiXJM16