Analisis Sentimen terhadap Penyelenggaraan Sea Games 2023 Kamboja pada Twitter Menggunakan Algoritma Naive Bayes

Farah Fadila Rahman, Frise Anesha Lutia, Ultach Enri
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Abstract

Southeast Asian Games or SEA Games is a Southeast Asian sporting event held every 2 years, where the participants are 11 member countries of the Association of Southeast Asian Nation (ASEAN). Cambodia was chosen as the host for the 2023 SEA Games. The implementation of the SEA Games in Cambodia experienced many controversies ranging from the inverted Indonesian flag to leaking lodging rooms for athletes. Social media Twitter became one of the places for netizens to express their opinions about the implementation of the SEA Games in Cambodia. This study aims to determine the level of tendency of positive, negative and neutral opinions through the sentiment analysis process. The sentiment analysis process is carried out using the Naive Bayes method, through five main stages, namely Data Selection, Preprocessing, Transformation, Data Mining, and Evaluation. The data used comes from Twitter users who use the hashtag "SEA Games Cambodia" then obtained data as many as 1595 tweets. The results of this study describe the results of Naive Bayes implementation and performance testing using confusion matrix obtained accuracy 66%, precision 70%, recall 66%, and f1-score 61%. and also obtained the results of the tendency of public opinion sentiment on Twitter with positive results as much as 49%, then negative results as much as 40% and neutral results as much as 11%.
使用 Naive Bayes 算法在 Twitter 上对 2023 年柬埔寨海上运动会组织情况进行情感分析
东南亚运动会(Southeast Asian Games 或 SEA Games)是每两年举办一次的东南亚体育盛会,由东南亚国家联盟(东盟)的 11 个成员国参加。柬埔寨被选为 2023 年东南亚运动会的主办国。在柬埔寨举办东南亚运动会经历了从印尼国旗倒置到运动员住宿房间漏水等诸多争议。社交媒体 Twitter 成为网民表达对柬埔寨举办东南亚运动会的意见的地方之一。本研究旨在通过情感分析过程确定正面、负面和中立意见的倾向程度。情感分析过程采用 Naive Bayes 方法,通过五个主要阶段进行,即数据选择、预处理、转换、数据挖掘和评估。所使用的数据来自使用 "柬埔寨东南亚运动会 "标签的 Twitter 用户,共获得 1595 条推文数据。本研究的结果描述了 Naive Bayes 的实施结果,并使用混淆矩阵进行了性能测试,结果显示准确率为 66%,精确率为 70%,召回率为 66%,f1-分数为 61%,同时还获得了 Twitter 上舆论情绪倾向的结果,其中正面结果占 49%,负面结果占 40%,中性结果占 11%。
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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