Paper: Sentiment Classification with Graph Co-Regularization

ACL ID C14-1126
Title Sentiment Classification with Graph Co-Regularization
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014

Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or neg- ative) of user-generated sentiment data (e.g., reviews, blogs). To obtain sentiment classifica- tion with high accuracy, supervised techniques require a large amount of manually labeled data. The labeling work can be time-consuming and expensive, which makes unsupervised (or semi- supervised) sentiment analysis essential for this application. In this paper, we propose a novel algorithm, called graph co-regularized non-negative matrix tri-factorization (GNMTF), from the geometric perspective. GNMTF assumes that if two words (or documents) are sufficiently close to each other, they tend to share the same sentiment polarity. To achieve this, we encode the geometric information by constructing the...