Comparison of Naive Bayes Smoothing Methods for Twitter Sentiment Analysis

被引：0
|
作者：
Ramadhani, Rif'at Ahdi [1 ]
Indriani, Fatma [1 ]
Nugrahadi, Dodon T. [1 ]
机构：
[1] Univ Lambung Mangkurat, Fac Math & Nat Sci, Banjarbaru, Indonesia
来源：
2016 INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER SCIENCE AND INFORMATION SYSTEMS (ICACSIS) | 2016年
关键词：
Sentiment Analysis; Data Mining; Naive Bayes; Smoothing; Laplace; Dirichlet; Absolute Discounting;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In sentiment analysis, the absence of sample features in the training data will lead to misclassification. Smoothing is used to overcome this problem. Previous studies show that there are differences in performance obtained by the various smoothing techniques against various types of data. In this paper, we compare the performance of Naive Bayes smoothing methods in improving the performance of sentiment analysis of tweets. The results indicated that Laplace smoothing is superior to Dirichlet smoothing and Absolute Discounting with the micro-average value of F1-Score 0.7234 and macro-average F1-Score 0.7182.
引用
收藏
页码：287 / 291
页数：5
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