Sentiment Analysis of Positive and Negative of YouTube Comments Using Naïve Bayes – Support Vector Machine (NBSVM) Classifier

Abbi Nizar Muhammad, S. Bukhori, P. Pandunata
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引用次数: 21

Abstract

Sentiment analysis on the YouTube video comments is a process of understanding, extracting, and processing textual data automatically to obtain sentiment information contained in one sentence of YouTube video comment. Text mining approach becomes the best alternative to interpret the meaning of each comment. The classification of positive and negative content becomes very important for the YouTube user to assess how meaningful the content that has been published is based on user opinion. Naïve Bayes and Support Vector Machine is extensively used as a basic line in tasks related to texts but the performance varies significantly in all variants, features, and numbers of data collection. Naïve Bayes is very good in classifying texts with the small number of data and document snippets while Support Vector is very good in classifying texts with relatively many numbers of data or full-length document. The combination of Naïve Bayes and Support Vector Machine produces better accuracy level and stronger performance with the use of a 7:3 scale of data that is 70% training data and 30% testing data. By producing the highest performance test values, namely precision of 91%, recall of 83% and flscore of 87%.
基于Naïve贝叶斯-支持向量机(NBSVM)分类器的YouTube正面和负面评论情感分析
YouTube视频评论情感分析是对文本数据进行自动理解、提取和处理的过程,以获取YouTube视频评论的一句话所包含的情感信息。文本挖掘方法成为解释每条评论含义的最佳选择。积极和消极内容的分类对于YouTube用户评估基于用户意见发布的内容的意义是非常重要的。Naïve贝叶斯和支持向量机被广泛用作文本相关任务的基线,但在所有变体、特征和数据收集数量上,性能差异很大。Naïve贝叶斯在对数据和文档片段数量较少的文本进行分类方面非常出色,而支持向量在对数据数量较多或全文文档的文本进行分类方面非常出色。Naïve贝叶斯和支持向量机的结合使用了7:3的数据比例,即70%的训练数据和30%的测试数据,产生了更好的准确率水平和更强的性能。通过产生最高的性能测试值,即精度为91%,召回率为83%,flscore为87%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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