Sentimen分析市场Bukalapak的KNN、决策树和朴素贝叶斯的比较

Elisa Nathania Halim, Baenil Huda, Anggi Elanda
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引用次数: 0

摘要

目前平台的数量使用户更容易提供评论,其中之一是谷歌Play商店上的Bukalapak应用程序。虽然负面和正面评价都会影响应用程序的价值,但用户也会受到应用程序情绪评价的影响。因此,有必要进行情绪分析,对负面评价和正面评价进行分类。本研究使用了1000条评论的评论数据,然后使用RapidMiner应用程序,使用三种方法对其进行分类,即KNN、Naive Bayes和决策树。KNN方法的结果获得了85.03%的准确度值、84.98%的准确度和100.00%的召回率,Naive Bayes方法获得了73.95%的准确度、100.00%的准确度,和69.26%的召回率;决策树方法获得了89.12%的准确率值、88.62%的准确度以及100.00%的回收率。这可以证明决策树方法优于KNN方法和Naive Bayes方法。
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Comparasion KNN, Decision Tree and Naïve Bayes for Sentimen Analysis Marketplace Bukalapak
The number of platforms currently makes it easier for users to provide reviews, one of which is the Bukalapak application on the Google Play Store. While both negative and positive reviews can influence the value of the app, users can also be affected by the app's sentiment reviews. Therefore it is necessary to carry out sentiment analysis to classify negative and positive reviews. This research uses review data of 1000 reviews and then classifies them using the RapidMiner application using three methods, namely KNN, Naive Bayes, and also the Decision Tree. The results of the KNN method obtained accuracy values of 85.03%, precision of 84.98%, and recall of 100.00%, then for the Naive Bayes method obtained accuracy values of 73.95%, precision of 100.00%, and recall of 69.26%, and for the Decision Tree method obtained 89.12% accuracy value, 88.62% precision, and 100.00% recall. this can prove that the Decision Tree method is superior to the KNN method and also the Naive Bayes method.
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