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

Yasuhiro SUZUKI
Hiroya TAKAMURA
Manabu OKUMURA

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E90-D    No.10    pp.1516-1522
Publication Date: 2007/10/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e90-d.10.1516
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System)
Category: 
Keyword: 
evaluative expression,  EM algorithm,  Naive Bayes classifier,  

Full Text: PDF(210.5KB)>>
Buy this Article


Summary: 
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.

open access publishing via