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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보통신학회JICCE Journal of information and communication convergence engineering Journal of information and communication convergence engineering Vol.17 No.1
- 발행연도
- 2019.3
- 수록면
- 41 - 48 (8page)
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초록· 키워드
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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%.
목차
Abstract
I. INTRODUCTION
II. SENTIMENT CLASSIFICATION METHODS
III. PROPOSED IMPROVED kNN METHOD
IV. EXPERIMENTS
V. REFLEXION AND DISCUSSION
VI. CONCLUSIONS
REFERENCES
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