{"title":"Analisis Pro Kontra Vaksin Covid 19 Menggunakan Sentiment Analysis Sumber Media Sosial Twitter","authors":"I. W. D. Gafatia, Novri Hadinata","doi":"10.47747/jpsii.v2i1.544","DOIUrl":null,"url":null,"abstract":"The development of information technology today has experienced very rapid growth. One of the developments in information technology, namely social media such as Twitter, Facebook, and Youtube, are some of the most popular communication media in today's society. Twitter is often used to express emotions about something, either praising or criticizing in the form of emotion. Human emotions can be categorized into five basic emotions, namely love, joy, sadness, anger, and fear. Twitter users' emotional tweets can be known as opinion or sentiment analysis (opinion analysis or sentiment analysis). Sentiment analysis is also carried out to see opinions or tendencies towards a problem or policy, whether they tend to have negative or positive opinions. The COVID-19 vaccine has become one of the discussions with a fairly high intensity on social media. Vaccine-related tweets have increased as government policies evolve. The responses of netizens also varied, ranging from clinical trials of vaccines, free vaccines, vaccine effectiveness, halal vaccines, to the implementation of vaccinations. This research produces a system that can analyze tweet sentiment related to the covid 19 vaccine in Indonesia where the tweet is obtained using the Twitter API. This system uses the Multinominal Naive Bayes method for the classification process.","PeriodicalId":339837,"journal":{"name":"Jurnal Pengembangan Sistem Informasi dan Informatika","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Jurnal Pengembangan Sistem Informasi dan Informatika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47747/jpsii.v2i1.544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
当今信息技术的发展经历了非常迅速的增长。信息技术的发展之一,即社交媒体,如Twitter, Facebook和Youtube,是当今社会中最受欢迎的交流媒体。Twitter经常被用来表达对某事的情感,以情感的形式赞扬或批评。人类的情绪可以分为五种基本情绪,即爱、喜悦、悲伤、愤怒和恐惧。推特用户的情绪推文可以称为观点分析或情绪分析(opinion analysis or sentiment analysis)。情绪分析也用于观察对问题或政策的看法或倾向,无论他们倾向于消极还是积极的观点。新冠病毒疫苗成为社交媒体上讨论强度较高的话题之一。随着政府政策的演变,与疫苗相关的推文也在增加。网民的反应也各不相同,从疫苗的临床试验、免费疫苗、疫苗有效性、清真疫苗到疫苗接种的实施。该研究开发了一个系统,可以分析印度尼西亚使用Twitter API获取的与covid - 19疫苗相关的推文情绪。该系统采用多项朴素贝叶斯方法进行分类。
Analisis Pro Kontra Vaksin Covid 19 Menggunakan Sentiment Analysis Sumber Media Sosial Twitter
The development of information technology today has experienced very rapid growth. One of the developments in information technology, namely social media such as Twitter, Facebook, and Youtube, are some of the most popular communication media in today's society. Twitter is often used to express emotions about something, either praising or criticizing in the form of emotion. Human emotions can be categorized into five basic emotions, namely love, joy, sadness, anger, and fear. Twitter users' emotional tweets can be known as opinion or sentiment analysis (opinion analysis or sentiment analysis). Sentiment analysis is also carried out to see opinions or tendencies towards a problem or policy, whether they tend to have negative or positive opinions. The COVID-19 vaccine has become one of the discussions with a fairly high intensity on social media. Vaccine-related tweets have increased as government policies evolve. The responses of netizens also varied, ranging from clinical trials of vaccines, free vaccines, vaccine effectiveness, halal vaccines, to the implementation of vaccinations. This research produces a system that can analyze tweet sentiment related to the covid 19 vaccine in Indonesia where the tweet is obtained using the Twitter API. This system uses the Multinominal Naive Bayes method for the classification process.