印度尼西亚社会对 COVID-19 增效疫苗的情绪分析

Dionisia Bhisetya Rarasati, Angelina Pramana Thenata, Afiyah Salsabila Arief
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引用次数: 0

摘要

COVID-19 大流行仍在包括印度尼西亚在内的多个国家发生。这次大流行是由冠状病毒引起的,该病毒已变异为多种病毒变种,如德尔塔病毒和奥米克隆病毒。截至 2022 年 2 月 9 日,印尼有 462.6936 万人被证实对 COVID-19 呈阳性反应。这一数字还在继续上升。印尼政府通过为公众接种加强型疫苗来防止这些病毒变种的传播。然而,这一疫苗接种计划在印尼人中引发了各种情绪。为了更好地抗击 COVID-19,政府需要立即了解这些情绪。基于这些问题,研究人员建议应用机器学习技术开发一个能够分析印尼人对加强免疫的情绪的系统。这项研究分为几个阶段:数据收集、数据标注、文本预处理、特征提取,以及使用不同内核(即线性内核、高斯径向基函数(RBF)内核和多项式内核)的支持向量机(SVM)算法的应用。此外,还使用 10 倍交叉验证和混淆矩阵测试了系统结果的准确性。使用的数据集是 681 条带有标签 "vaksinbooster "的推文。数据集由两类组成:负(0)和正(1)。结果显示,阳性推文数量较多,为 554 条,而阴性推文为 127 条,这表明数据对加强型疫苗具有积极意义。此外,数据集分为训练数据 545 条和测试数据 136 条。此外,本研究的测试结果显示,采用多项式核的 SVM 算法在 10 倍交叉验证的评估中获得了最高的准确率,即 79.22%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Indonesian Society’s Sentiment Analysis Against the COVID-19 Booster Vaccine
The COVID-19 pandemic is still occurring in various countries, including Indonesia. This pandemic is caused by the coronavirus, which has mutated into multiple virus variants, such as Delta and Omicron. As of 9 February 2022, 4,626,936 people were confirmed positive for COVID-19 in Indonesia. This number continues to rise. The Indonesian government has prevented the spread of these virus variants by introducing booster vaccines to the public. However, this vaccination program has caused various sentiments among Indonesians. To optimize efforts to combat COVID-19, the government needs to know these sentiments immediately. Based on these problems, the researcher proposes the application of machine learning technology to develop a system that can analyze the sentiments of the Indonesians toward the booster vaccine. This research has several stages: data collection, data labeling, text preprocessing, feature extraction, and application of the support vector machine (SVM) algorithm using various kernels, namely the linear kernel, Gaussian radial basis function (RBF) kernel, and polynomial kernel. Furthermore, the results of the system were tested for accuracy using a 10-fold cross validation and confusion matrix. The dataset used was 681 tweets with the hashtag “vaksinbooster.” The dataset consists of two classes: negative (0) and positive (1). The results showed that the data were positive for the booster vaccine, as evidenced by the higher number of positive tweets, with 554 data, compared to 127 negative tweets. In addition, the dataset was divided into training data of 545 and testing data of 136. In addition, the test results of this study revealed that the SVM algorithm with the polynomial kernel, which was evaluated with 10-fold cross validation, yielded the highest level of accuracy, namely 79.22%.
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