2020년 컴퓨터종합학술대회
Current Result Document :
| 한글제목(Korean Title) |
문서 감정 분류를 위한 개선된 공동 학습 접근 |
| 영문제목(English Title) |
An improved co-training approach for document Sentiment classification |
| 저자(Author) |
Huynh Cong Viet Ngu
Keon Myung Lee
이건명
Jawad Khan
Aftab Alam
Muhammad Numan Khan
Irfan Ullah
Muhammad Umair
Umair Qudus
Tariq Habib Afridi
Sung Soo Park
Young-Koo Lee
자와드 칸
아프타 발람
무함마드 누만 칸
이르판 울라
무하마드 우매르
구두스 우매르
타리크 하비브 아프 리디
박성수
이영구
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| 원문수록처(Citation) |
VOL 47 NO. 01 PP. 0791 ~ 0793 (2020. 07) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Sentiment Analysis (SA) is an active research area that is used to automatically extract useful information from the user-generated content (UGC) to classify into positive and negative classes. Recently, various machine-learning techniques, such as supervised machine learning, semi-supervised learning, and lexicon scoring for SA have been proposed. A high-quality training data is vital to learn a sentiment classifier for textual sentiment classification, but due to various domains, the labeled data for each domain is scarce or unavailable. The manual construction of labeled corpora is a timeconsuming and laborious task because of the unstructured and unorganized nature of data. In order to address this issue, in this paper, we propose an improved co-training approach based on the n-gram and word2vec model for sentiment classification. Co-training is a semi-supervised learning approach that has effective applications in textual sentiment classification. The empirical evaluation of movie review datasets shows that the proposed approach outperforms existing techniques in terms of classification accuracy. |
| 키워드(Keyword) |
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