Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm

Debby Erce Sondakh, Raissa C Maringka, Ferlien P Ayorbaba, Joanne S. C. B. T. Mangi, Stenly Richard Pungus
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Abstract

As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.
基于决策树算法的BRImo手机银行应用情感挖掘用户评论
随着消费者交易偏好从模拟转向数字,银行被迫以移动银行的形式开发数字交易。手机银行的用户提供了关于应用程序可用性的反馈。用户的意见可能是情绪化的。情绪影响一个人的言行。情绪是一个人在快乐或不快乐时的行为反应。因此,一个人的情绪的表现,无论是以面部表情、口头交流、书面文本还是判断的形式,都可以作为帮助决策的信息来源。本研究的目的是将情感挖掘应用于BRImo应用程序的用户评价分析。截至2022年8月,BRImo应用程序是印度尼西亚最受欢迎的三个平台之一,在Play Store上共有80万条评论。情感挖掘可以用来分析用户在评论区表达的四类情绪:高兴、生气、悲伤和害怕。根据BRImo用户的评价,使用决策树算法对快乐、悲伤、害怕和愤怒情绪进行分类。使用决策树来管理大型数据类别集是有效的。获得的数据集包括2959个快乐类、2196个悲伤类、387个愤怒类和81个恐惧类。根据分析的结果,相当多的BRImo应用程序用户在他们的评价中表达了积极的情绪,这表明了快乐的情绪。决策树算法的性能指标为84.5%,灵敏度为85.5%,精度为84.4%。
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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