Mining Customers Opinion on Services and Applications of Mobile Payment Companies in Indonesia Using Sentiment Analysis Approach

Nadhila Idzni Prabaningtyas, I. Surjandari, Enrico Laoh
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引用次数: 1

Abstract

The development of technology and digital has also increased the ease of accessing the internet. One aspect of daily life that are affected by the adoption of technology and the internet is the field of payment transactions. Payment transactions are inseparable from everyday life. At this time with the development of technology, payment transactions can be done with the more practical, easy, safe and convenient. The technology is called Financial Technology. Mobile payment is a service that is part of financial technology. The aspects contained in the mobile payment are top up, transfers, cash withdrawals, online payment, and offline payments. Classifications of reviews from Twitter are classified using Support Vector Machine. The results of this study are Go-Pay and OVO must pay attention to every aspect and improve every aspect, of course, to increase customer satisfaction. The accuracy level of the classification model produced for bigram is 92% (Go-Pay) and 93% (OVO). It also shows that sentiment analysis using bigram can improve accuracy level.
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使用情感分析方法挖掘印尼客户对移动支付公司服务和应用的意见
科技和数字的发展也增加了访问互联网的便利性。日常生活中受科技和互联网影响的一个方面是支付交易领域。支付交易与日常生活密不可分。此时随着技术的发展,支付交易可以用更加实用、简单、安全、方便的方式来完成。这项技术被称为金融技术。移动支付是一项服务,是金融科技的一部分。移动支付包含充值、转账、提现、线上支付、线下支付等方面。使用支持向量机对Twitter上的评论进行分类。本研究的结果是Go-Pay和OVO必须关注每一个方面,提高每一个方面,当然,要提高客户满意度。对bigram生成的分类模型的准确率为92% (Go-Pay)和93% (OVO)。同时也证明了使用双元图进行情感分析可以提高情感分析的准确率。
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