Semi-Supervised Learning to Classify Evaluative Expressions from Labeled and Unlabeled Texts

• SUZUKI Yasuhiro

  Department of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• TAKAMURA Hiroya

  Precision and Intelligence Laboratory, Tokyo Institute of Technology

• OKUMURA Manabu

  Precision and Intelligence Laboratory, Tokyo Institute of Technology

Abstract

In this paper, we present a method to automatically acquire a large-scale vocabulary of evaluative expressions from a large corpus of blogs. For the purpose, this paper presents a semi-supervised method for classifying 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, our semi-supervised method can classify evaluative expressions in a corpus by their polarities, starting from a very small set of seed training examples and using contextual information in the sentences the expressions belong to. Our experimental results with real Weblog data as our corpus show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions. We also show that a reasonable amount of evaluative expressions can be really acquired.

Journal

• IEICE transactions on information and systems

  IEICE transactions on information and systems 90 (10), 1516-1522, 2007-10-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• evaluative expression
• EM algorithm
• Naive Bayes classifier

Details

• CRID: 1570854177591321984
• NII Article ID: 110007538555
• NII Book ID: AA10826272
• ISSN: 09168532
• Text Lang: en
• Data Source:
  • CiNii Articles