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
전통적인 텍스트 수준의 감성 분석 방법은 일반적으로 객체나 속성에 해당하는 감성 경향을 무시하는 문제를 목표로 합니다. 본 논문에서는 구문 규칙 매칭과 심층 의미론을 기반으로 한 새로운 2단계 세분화된 텍스트 수준 감성 분석 모델을 제안합니다. 세분화된 감성분석의 특징과 난이도 분석을 바탕으로 2단계 세분화된 감성분석 알고리즘 프레임워크를 구축한다. 첫 번째 단계에서는 사전 객체와 의견을 얻기 위해 일치하는 구문 규칙을 기반으로 객체와 해당 의견을 추출합니다. 보다 정확한 객체와 의견을 추출하기 위한 심층 의미망 기반의 두 번째 단계입니다. 추출 결과에 일치해야 할 개체와 의견이 여러 개 포함되어 있다는 문제를 해결하기 위해 정확한 쌍별 일치를 달성하기 위해 최소 어휘 분리 거리를 기반으로 하는 개체-의견 일치 알고리즘을 제안합니다. 마지막으로 제안된 알고리즘은 실용성과 효율성을 입증하기 위해 여러 공개 데이터 세트에서 평가됩니다.
Weizhi LIAO
University of Electronic Science and Technology of China
Yaheng MA
University of Electronic Science and Technology of China
Yiling CAO
University of Electronic Science and Technology of China
Guanglei YE
University of Electronic Science and Technology of China
Dongzhou ZUO
University of Electronic Science and Technology of China
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부
Weizhi LIAO, Yaheng MA, Yiling CAO, Guanglei YE, Dongzhou ZUO, "Two-Stage Fine-Grained Text-Level Sentiment Analysis Based on Syntactic Rule Matching and Deep Semantic" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 8, pp. 1274-1280, August 2021, doi: 10.1587/transinf.2020BDP0018.
Abstract: Aiming at the problem that traditional text-level sentiment analysis methods usually ignore the emotional tendency corresponding to the object or attribute. In this paper, a novel two-stage fine-grained text-level sentiment analysis model based on syntactic rule matching and deep semantics is proposed. Based on analyzing the characteristics and difficulties of fine-grained sentiment analysis, a two-stage fine-grained sentiment analysis algorithm framework is constructed. In the first stage, the objects and its corresponding opinions are extracted based on syntactic rules matching to obtain preliminary objects and opinions. The second stage based on deep semantic network to extract more accurate objects and opinions. Aiming at the problem that the extraction result contains multiple objects and opinions to be matched, an object-opinion matching algorithm based on the minimum lexical separation distance is proposed to achieve accurate pairwise matching. Finally, the proposed algorithm is evaluated on several public datasets to demonstrate its practicality and effectiveness.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020BDP0018/_p
부
@ARTICLE{e104-d_8_1274,
author={Weizhi LIAO, Yaheng MA, Yiling CAO, Guanglei YE, Dongzhou ZUO, },
journal={IEICE TRANSACTIONS on Information},
title={Two-Stage Fine-Grained Text-Level Sentiment Analysis Based on Syntactic Rule Matching and Deep Semantic},
year={2021},
volume={E104-D},
number={8},
pages={1274-1280},
abstract={Aiming at the problem that traditional text-level sentiment analysis methods usually ignore the emotional tendency corresponding to the object or attribute. In this paper, a novel two-stage fine-grained text-level sentiment analysis model based on syntactic rule matching and deep semantics is proposed. Based on analyzing the characteristics and difficulties of fine-grained sentiment analysis, a two-stage fine-grained sentiment analysis algorithm framework is constructed. In the first stage, the objects and its corresponding opinions are extracted based on syntactic rules matching to obtain preliminary objects and opinions. The second stage based on deep semantic network to extract more accurate objects and opinions. Aiming at the problem that the extraction result contains multiple objects and opinions to be matched, an object-opinion matching algorithm based on the minimum lexical separation distance is proposed to achieve accurate pairwise matching. Finally, the proposed algorithm is evaluated on several public datasets to demonstrate its practicality and effectiveness.},
keywords={},
doi={10.1587/transinf.2020BDP0018},
ISSN={1745-1361},
month={August},}
부
TY - JOUR
TI - Two-Stage Fine-Grained Text-Level Sentiment Analysis Based on Syntactic Rule Matching and Deep Semantic
T2 - IEICE TRANSACTIONS on Information
SP - 1274
EP - 1280
AU - Weizhi LIAO
AU - Yaheng MA
AU - Yiling CAO
AU - Guanglei YE
AU - Dongzhou ZUO
PY - 2021
DO - 10.1587/transinf.2020BDP0018
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2021
AB - Aiming at the problem that traditional text-level sentiment analysis methods usually ignore the emotional tendency corresponding to the object or attribute. In this paper, a novel two-stage fine-grained text-level sentiment analysis model based on syntactic rule matching and deep semantics is proposed. Based on analyzing the characteristics and difficulties of fine-grained sentiment analysis, a two-stage fine-grained sentiment analysis algorithm framework is constructed. In the first stage, the objects and its corresponding opinions are extracted based on syntactic rules matching to obtain preliminary objects and opinions. The second stage based on deep semantic network to extract more accurate objects and opinions. Aiming at the problem that the extraction result contains multiple objects and opinions to be matched, an object-opinion matching algorithm based on the minimum lexical separation distance is proposed to achieve accurate pairwise matching. Finally, the proposed algorithm is evaluated on several public datasets to demonstrate its practicality and effectiveness.
ER -