The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 이해관계자 설명을 비교하여 뉴스 기사의 편견을 분석하는 새로운 이해관계자 마이닝 메커니즘을 제안합니다. 우리의 메커니즘은 이익이 종종 언론사의 편견을 유발한다는 가정에 기반을 두고 있습니다. 우리가 사용하는 용어에서 "이해관계자"는 뉴스 기사에 설명된 이벤트의 참가자로서 기사의 다른 참가자와 관계를 맺고 있어야 합니다. 우리의 접근 방식은 이해관계자, 이해관계자의 관심, 각 이해관계자의 설명적 극성이라는 세 가지 측면에서 기사의 편향을 밝히려고 시도합니다. 이해관계자와 그들의 관심사에 대한 마이닝은 문장 구조 분석과 우리가 개발한 어휘 리소스인 RelationshipWordNet의 사용을 통해 달성됩니다. 이해관계자 설명의 극성을 분석하기 위해 어휘 리소스 SentiWordNet을 기반으로 한 의견 마이닝 방법을 제안합니다. 분석 결과, 상호 이익을 공유하는 이해관계자를 그룹화하고 이해관계자의 이익을 대표하기 위한 이해관계자 관계 그래프를 구축합니다. 또한 마이닝 메커니즘을 기반으로 뉴스 비교를 위해 개발한 응용 시스템에 대해서도 설명합니다. 본 논문에서는 제안된 방법을 검증하기 위한 몇 가지 실험 결과를 제시합니다.
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부
Tatsuya OGAWA, Qiang MA, Masatoshi YOSHIKAWA, "News Bias Analysis Based on Stakeholder Mining" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 3, pp. 578-586, March 2011, doi: 10.1587/transinf.E94.D.578.
Abstract: In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a "stakeholder" is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of RelationshipWordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource SentiWordNet. As a result of analysis, we construct a relations graph of stakeholders to group stakeholders sharing mutual interests and to represent the interests of stakeholders. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents some experimental results to validate the proposed methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.578/_p
부
@ARTICLE{e94-d_3_578,
author={Tatsuya OGAWA, Qiang MA, Masatoshi YOSHIKAWA, },
journal={IEICE TRANSACTIONS on Information},
title={News Bias Analysis Based on Stakeholder Mining},
year={2011},
volume={E94-D},
number={3},
pages={578-586},
abstract={In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a "stakeholder" is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of RelationshipWordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource SentiWordNet. As a result of analysis, we construct a relations graph of stakeholders to group stakeholders sharing mutual interests and to represent the interests of stakeholders. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents some experimental results to validate the proposed methods.},
keywords={},
doi={10.1587/transinf.E94.D.578},
ISSN={1745-1361},
month={March},}
부
TY - JOUR
TI - News Bias Analysis Based on Stakeholder Mining
T2 - IEICE TRANSACTIONS on Information
SP - 578
EP - 586
AU - Tatsuya OGAWA
AU - Qiang MA
AU - Masatoshi YOSHIKAWA
PY - 2011
DO - 10.1587/transinf.E94.D.578
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2011
AB - In this paper, we propose a novel stakeholder mining mechanism for analyzing bias in news articles by comparing descriptions of stakeholders. Our mechanism is based on the presumption that interests often induce bias of news agencies. As we use the term, a "stakeholder" is a participant in an event described in a news article who should have some relationships with other participants in the article. Our approach attempts to elucidate bias of articles from three aspects: stakeholders, interests of stakeholders, and the descriptive polarity of each stakeholder. Mining of stakeholders and their interests is achieved by analysis of sentence structure and the use of RelationshipWordNet, a lexical resource that we developed. For analyzing polarities of stakeholder descriptions, we propose an opinion mining method based on the lexical resource SentiWordNet. As a result of analysis, we construct a relations graph of stakeholders to group stakeholders sharing mutual interests and to represent the interests of stakeholders. We also describe an application system we developed for news comparison based on the mining mechanism. This paper presents some experimental results to validate the proposed methods.
ER -