Sentiment Analysis Of Vaccination Using The K-Nearest Neighbor Algorithm

F. Aryanto, Ahmad Fauzi, Anis Fitri Nur Masruriyah, April Lia Hananto, Darmansyah
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Abstract

The outbreak of the COVID-19 virus that attacked the country of Indonesia made the government implement a policy, namely giving vaccinations. Since the announcement of the government's policy on administering the COVID-19 vaccine in January 2021, there have been a lot of discussions, especially on social media. One of the social media that is widely used by the public for opinions is Twitter. this has resulted in Twitter becoming a medium for expressing people's thoughts on administering the COVID-19 vaccine. Opinions generated can be positive, negative, or neutral towards vaccine administration. Based on the description of the problem, the study aims to analyze the sentiments of netizens regarding government policies in administering vaccinations. The method used is the KNN Algorithm to carry out sentiment analysis through public opinion on Twitter social media. The results sentiment with the highest value is positive which has more than 150 sentences. Then it produces an accuracy of 86.6% Precision of 85% and Recalls of 81%.
基于k近邻算法的疫苗接种情感分析
由于新冠肺炎疫情的爆发,印度尼西亚政府实施了一项政策,即接种疫苗。自政府于2021年1月公布新冠病毒疫苗接种政策以来,特别是在社交媒体上引起了很多讨论。公众广泛使用的社交媒体之一是Twitter。这使得推特成为了人们表达对新冠病毒疫苗接种想法的媒介。对疫苗接种产生的意见可以是积极的、消极的或中立的。在问题描述的基础上,本研究旨在分析网民对政府疫苗接种政策的看法。使用的方法是KNN算法,通过Twitter社交媒体上的民意进行情绪分析。结果情绪值最高的是正面情绪,超过150句。然后,它产生的准确率为86.6%,精密度为85%,召回率为81%。
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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