{"title":"Analisis Sentimen Ulasan Aplikasi Wetv Untuk Peningkatan Layanan Menggunakan Metode Support Vector Machine","authors":"Rezky Abdillah, Elin Haerani, Reski Mai Candra","doi":"10.47065/josh.v4i3.3353","DOIUrl":null,"url":null,"abstract":"Wetv is an online streaming media that has been running since 2019. Wetv has many user reviews from various applications. The rating consists of positive, neutral and negative. The response is used to determine sentiment by using the support vector machine classification method. This study took 12,000 comments from the Google Play Store, this study used preprocessing namely, cleaning, case folding, tokenizing, normalization, stopword removal, and steaming, then to the TF-IDF stage and the final results were tested with a fusion matrix with the Python program, the score results highest from the acquisition test process with accuracy of 0.76%, precision of 0.77%, recall of 0.79%, and f1 score of 0.78, in a dataset of 90% training data and 10% test data. Based on the research results of the Support Vector Machine method which is known to be good in the process of requesting negative responses on WeTV.","PeriodicalId":233506,"journal":{"name":"Journal of Information System Research (JOSH)","volume":"9 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Information System Research (JOSH)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.47065/josh.v4i3.3353","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Wetv is an online streaming media that has been running since 2019. Wetv has many user reviews from various applications. The rating consists of positive, neutral and negative. The response is used to determine sentiment by using the support vector machine classification method. This study took 12,000 comments from the Google Play Store, this study used preprocessing namely, cleaning, case folding, tokenizing, normalization, stopword removal, and steaming, then to the TF-IDF stage and the final results were tested with a fusion matrix with the Python program, the score results highest from the acquisition test process with accuracy of 0.76%, precision of 0.77%, recall of 0.79%, and f1 score of 0.78, in a dataset of 90% training data and 10% test data. Based on the research results of the Support Vector Machine method which is known to be good in the process of requesting negative responses on WeTV.
微视网是一家自2019年开始运营的在线流媒体。Wetv有许多来自不同应用程序的用户评论。评级分为正面、中性和负面。通过支持向量机分类方法,将响应用于确定情感。本研究从Google Play Store中获取了12000条评论,本研究对其进行了预处理,即清洗、案例折叠、标记化、归一化、停止词去除和蒸析,然后进入TF-IDF阶段,并使用Python程序对最终结果进行融合矩阵测试,在90%训练数据和10%测试数据的数据集中,采集测试过程的得分最高,准确率为0.76%,精密度为0.77%,召回率为0.79%,f1得分为0.78。基于支持向量机方法的研究结果,支持向量机方法在微电视上请求否定回复的过程中表现良好。