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Authors: Yoshimi Suzuki and Fumiyo Fukumoto

Affiliation: University of Yamanashi, Japan

ISBN: 978-989-758-158-8

Keyword(s): Opinion Extraction, Editorial Articles.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Knowledge Acquisition ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems

Abstract: Opinion extraction supports various tasks such as sentiment analysis in user reviews for recommendations and editorial summarization. In this paper, we address the problem of opinion extraction from newspaper editorials. To extract author’s opinion, we used context information addition to the features within a single sentence only. Context information are a location of the target sentence, and its preceding, and succeeding sentences. We defined the opinion extraction task as a sequence labeling problem, using conditional random fields (CRF). We used Japanese newspaper editorials in the experiments, and used multiple combination of features of CRF to reveal which features are effective for opinion extraction. The experimental results show the effectiveness of the method, especially, predicate expression, location and previous sentence are effective for opinion extraction.

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Paper citation in several formats:
Suzuki, Y. and Fukumoto, F. (2015). Opinion Extraction from Editorial Articles based on Context Information.In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015) ISBN 978-989-758-158-8, pages 375-380. DOI: 10.5220/0005633503750380

@conference{keod15,
author={Yoshimi Suzuki. and Fumiyo Fukumoto.},
title={Opinion Extraction from Editorial Articles based on Context Information},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)},
year={2015},
pages={375-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005633503750380},
isbn={978-989-758-158-8},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, (IC3K 2015)
TI - Opinion Extraction from Editorial Articles based on Context Information
SN - 978-989-758-158-8
AU - Suzuki, Y.
AU - Fukumoto, F.
PY - 2015
SP - 375
EP - 380
DO - 10.5220/0005633503750380

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