Semi-Supervised Learning to Classify Evaluative Expressions from Labeled and Unlabeled Texts
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.10 pp.1516-1522
- Publication Date
- 2007/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.10.1516
- Type of Manuscript
- Special Section PAPER (Special Section on Knowledge, Information and Creativity Support System)
- Category
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Yasuhiro SUZUKI, Hiroya TAKAMURA, Manabu OKUMURA, "Semi-Supervised Learning to Classify Evaluative Expressions from Labeled and Unlabeled Texts" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 10, pp. 1516-1522, October 2007, doi: 10.1093/ietisy/e90-d.10.1516.
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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.10.1516/_p
Copy
@ARTICLE{e90-d_10_1516,
author={Yasuhiro SUZUKI, Hiroya TAKAMURA, Manabu OKUMURA, },
journal={IEICE TRANSACTIONS on Information},
title={Semi-Supervised Learning to Classify Evaluative Expressions from Labeled and Unlabeled Texts},
year={2007},
volume={E90-D},
number={10},
pages={1516-1522},
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.},
keywords={},
doi={10.1093/ietisy/e90-d.10.1516},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Semi-Supervised Learning to Classify Evaluative Expressions from Labeled and Unlabeled Texts
T2 - IEICE TRANSACTIONS on Information
SP - 1516
EP - 1522
AU - Yasuhiro SUZUKI
AU - Hiroya TAKAMURA
AU - Manabu OKUMURA
PY - 2007
DO - 10.1093/ietisy/e90-d.10.1516
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2007
AB - 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.
ER -
English
