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http://dx.doi.org/10.6109/jicce.2019.17.1.41

An Improved Text Classification Method for Sentiment Classification  

Wang, Guangxing (Department of Information Technology Center, Jiujiang University)
Shin, Seong Yoon (School of Computer Information & Communication Engineering, Kunsan National University)
Publication Information
Journal of information and communication convergence engineering / v.17, no.1, 2019 , pp. 41-48 More about this Journal
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%.
Keywords
Sentiment Analysis; Machine Learning; Text Classification; k-Nearest Neighbor Method;
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