JICCE (한국정보통신학회)
Current Result Document :
| 한글제목(Korean Title) |
An Improved Text Classification Method for Sentiment Classification |
| 영문제목(English Title) |
An Improved Text Classification Method for Sentiment Classification |
| 저자(Author) |
Guangxing Wang
Seong Yoon Shin
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| 원문수록처(Citation) |
VOL 17 NO. 01 PP. 0041 ~ 0048 (2019. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%. |
| 키워드(Keyword) |
Sentiment Analysis
Machine Learning
Text Classification
k-Nearest Neighbor Method
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| 파일첨부 |
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