情绪分析——印尼社会对抗奥密克戎病毒的朴素贝叶斯方法

Aziz Musthafa, Tri Harmini, A. Setiawan, Nur Aini Shofiya Asy’ari
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引用次数: 0

摘要

【利用朴素贝叶斯方法分析印度尼西亚奥密克戎病毒的新闻情绪和民意】新冠肺炎病毒继续变异形成新的变种。最后一个检测到的变异株是奥密克戎变异株,被称为B.1.1.529变异株。该变种于2021年11月24日在南非首次报告,目前已在全球传播。2022年7月,奥密克戎病例激增。这引发了很多关于奥密克戎病毒的舆论,尤其是在社交媒体上。这项研究旨在将推特和YouTube社交媒体上对奥密克戎病毒出现的舆论分为积极、消极和中立三类。本研究中使用的方法是朴素贝叶斯算法。朴素贝叶斯是一种可以用来对公众舆论情绪进行分类的方法。使用天真贝叶斯的情绪分析研究结果产生了0.82%的准确率。然后对该模型进行了测试,以读取2022年10月5日至10月27日推特上的民意。推特用户对关键字新冠肺炎19的情绪结果以积极情绪为主,比例为85%。以奥密克戎为关键词的情绪仍然以积极情绪为主,占49%。据指出,2022年10月的数据分类结果意味着人们对奥密克戎病毒的消失更加乐观。从那时起,这项研究可以通过添加数据、使用不同的算法或实现现有算法来改进
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Analisis Sentimen Opini Masyarakat Terhadap Virus Omicron Di Indonesia Menggunakan Metode Naïve Bayes
[Analysis Of News Sentiment And Public Opinion On Omicron Virus In Indonesia Using The Naïve Bayes Method] The Covid-19 virus continues to mutate to form new variants. The last detected variant, the Omicron variant, is known as the B.1.1.529 variant. This variant was first reported from South Africa on 24 November 2021 and has now spread worldwide. In July 2022 Omicron cases experienced a spike. This has led to a lot of public opinion, especially on social media, about the omicron virus. This study aims to classify public opinion on the emergence of the Omicron virus on Twitter and YouTube social media into positive, negative and neutral classes. The method used in this study is the naïve Bayes algorithm. Naïve Bayes is a method that can be used to classify public opinion sentiment. The results of sentiment analysis research using naïve Bayes produce an accuracy rate of 0.82%. Then the model was tested to read public opinion on Twitter from 5 October 2022 to 27 October 2022. The results for Twitter user sentiment on the keyword Covid 19 were dominated by positive sentiment with a percentage of 85%. And sentiment with the keyword Omicron is still dominated by positive sentiment with a percentage of 49%. It was stated that the results of the classification on data for October 2022 meant that people were much more optimistic about the disappearance of the Omicron virus. Henceforth this research can be improved by adding data or using a different algorithm or implementing an existing algorithm
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