Application of Semi-supervised Learning to Evaluative Expression Classification

Abstract. We propose to use semi-supervised learning methods to classify evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, the semisupervised method that we use can classify evaluative expressions in a corpus by their polarities. This can be accomplished starting from a very small set of seed training examples and using contextual information in the sentences to which the expressions belong. Our experimental results with actual Weblog data show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions.

• Type: Conference
• Year: 2006
• Where: CICLING
• Authors: Yasuhiro Suzuki, Hiroya Takamura, Manabu Okumura
• Keywords: CICLING 2006, Evaluative Expressions, Natural Language Processing, Semi-supervised Learning Methods, Unfavorable Opinions