使用朴素贝叶斯和Xgboost分类器算法对Twitter上关于印度尼西亚Covid-19疫苗接种的情绪分析

Alvin Irwanto, L. Goeirmanto
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引用次数: 1

摘要

这场席卷全球的大流行给我们的生活带来了巨大影响。但过了一段时间,这种情况似乎就要结束了,因为疫苗已经研制出来了。对此,一些人在社交媒体上表达了他们对这种疫苗接种的看法,例如在Twitter上以推文的形式。作者使用这些观点或推文作为情感分析材料来确定对这种疫苗接种的评估。本研究中的推文数据是通过使用Twitter API和Python编程语言进行数据抓取获得的。本例中使用的变量是公共tweet及其情绪。该情感分析过程使用朴素贝叶斯分类器的分类方法,并将与XGBoost分类器算法进行比较。这项研究的结果表明,人们更有可能对这种疫苗作出积极的反应。在这种情况下,朴素贝叶斯分类器的ROC - AUC得分为0.95,运行时间为134 ms,而XGBoost分类器算法的ROC - AUC得分为0.882,运行时间为1分59秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment Analysis From Twitter About Covid-19 Vaccination in Indonesia Using Naive Bayes and Xgboost Classifier Algorithm
The pandemic that hit the world has greatly impacted our life. But after some time, it seems that it will be going to end because the vaccine has already been made. In response to this, some people expressed their opinions about this vaccination on social media, for example, in the form of tweets on Twitter. The authors use those opinions or tweets as sentiment analysis material to determine the assessment of this vaccination. The tweet data in this study was obtained through data crawling using the Twitter API with the Python programming language. The variables used in this case are public tweets and their sentiments. This sentiment analysis process uses the Classification method with the Naive Bayes Classifier and will be compared with the XGBoost Classifier algorithm. The results of this study indicate that people are more likely to respond positively to this vaccination. In this case, the Naive Bayes Classifier got better performance with 0.95 from ROC - AUC Score and 134 ms in runtime compared to the XGBoost Classifier algorithm with 0.882 in ROC - AUC Score and 1 minute and 59 seconds in runtime.
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