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A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews

机译：基于数字付款评论检测情感和情感的半监督方法

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This paper investigates the sentiment and emotion of digital payment application consumers using a hybrid approach consisting of both supervised and unsupervised machine learning techniques. Support vector machine, random forest and Naive Bayes were modeled for sentiment and emotion analyses, whereas latent Dirichlet allocation was administered to identify top emerging topics based on English textual reviews from three digital payment applications. Random forest produced the best results for sentiment (F1 score = 73.8%; Cohen's Kappa = 52.2%) and emotion (F1 score = 58.8%; Cohen's Kappa = 44.7%) analyses based on a tenfold cross-validation. Latent Dirichlet allocation revealed best clusters atk = 5 and items = 25, with the top topics being App Service, Transaction, Reload Features, Connectivity and Reward. Findings are presented and discussed in general and also based on each application.
机译：本文通过由监督和无监督机器学习技术组成的混合方法来调查数字付款应用消费者的情感和情感。支持向量机，随机森林和天真贝叶斯被建模为情感和情感分析，而潜在的Dirichlet分配被管理以确定基于三个数字付款应用的英语文本审查的顶级新兴主题。随机森林为情感产生了最佳结果（F1得分= 73.8％; Cohen的Kappa = 52.2％）和情感（F1得分= 58.8％;科恩的Kappa = 44.7％）基于十倍交叉验证分析。潜在的Dirichlet分配显示最佳群集ATK = 5和项目= 25，顶主题是应用服务，交易，重新加载功能，连接和奖励。一般来说并讨论了调查结果，并基于每个应用程序讨论。

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