A. Wibawa, Adjie Rosyidin, Fitriana Kurniawati, Gwinny Tirza Rarastri, I. Zaeni, S. Suyono, Agung Bella Putra Utama, F. Dwiyanto
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引用次数: 0
摘要
印度尼西亚是一个拥有多种文化艺术的国家,皮影戏(Wayang)就是其中之一。以舞台化、简单和简约的方式表演的瓦扬被称为 "Wayang Climen"。自 COVID-19 大流行以来,Wayang Climen 一直在表演,作为一种既能继续工作,又能遵守卫生协议的解决方案。通过 YouTube 社交媒体的使用,吸引了人们观看并通过评论提供意见。这种意见是有益的,可通过情感分析信息将其分为正面、负面和中性意见,以此作为可行性研究。情感分析可确定一个人的观点和对观点性句子的倾向。使用的方法有随机森林(RF)、支持向量机(SVM)和奈夫贝叶斯(NB)。数据集来自 YouTube 上 Dalang Seno 和 Ki Seno Nugroho 的评论。SVM 的准确率最高(70.29%)。积极情绪显示了公众对瓦扬克里门表演的赞赏,尽管表演的舞台很密集,但这最终代表了表演。这项研究有助于在充满挑战的时代有效利用数字平台进行文化保护和观众参与,展示了传统艺术和娱乐创新解决方案的潜力。
Mining the public sentiment for wayang climen preservation and promotion
Indonesia is a country that has a variety of cultural arts, one of which is shadow puppetry (Wayang). Wayang, in a staged, simple, and minimalist manner, is called Wayang Climen. Wayang Climen has been performed since the COVID-19 pandemic as a solution to keep working while still complying with health protocols. Utilization through YouTube social media attracts people to watch and provide opinions through comments. This opinion is beneficial and can be used as a feasibility study through sentiment analysis information classified as positive, negative, and neutral opinions. Sentiment analysis determines a person's opinion and tendency to opinionated sentences. The methods used are Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The dataset comes from YouTube comments of Dalang Seno and Ki Seno Nugroho. The best accuracy is generated by SVM (70.29%). The positive sentiment shows the public's appreciation for the Wayang Climen performance, which ultimately represents the performance even though it is staged densely. This research contributes to effectively utilizing digital platforms for cultural preservation and audience engagement during challenging times, demonstrating the potential for innovative solutions in traditional arts and entertainment.