{"title":"Investigation and prediction of users' sentiment toward food delivery apps applying machine learning approaches","authors":"Md. Shamim Hossain, Humaira Begum, Md. Abdur Rouf, Md. Mehedul Islam Sabuj","doi":"10.1108/jcmars-12-2022-0030","DOIUrl":null,"url":null,"abstract":"PurposeThe goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).Design/methodology/approachUsing Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.FindingsAccording to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.Practical implicationsThe findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.Originality/valueThe current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.","PeriodicalId":333619,"journal":{"name":"Journal of Contemporary Marketing Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Contemporary Marketing Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jcmars-12-2022-0030","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
PurposeThe goal of the current research is to use different machine learning (ML) approaches to examine and predict customer reviews of food delivery apps (FDAs).Design/methodology/approachUsing Google Play Scraper, data from five food delivery service providers were collected from the Google Play store. Following cleaning the reviews, the filtered texts were classified as having negative, positive, or neutral sentiments, which were then scored using two unsupervised sentiment algorithms (AFINN and Valence Aware Dictionary for sentiment Reasoning (VADER)). Furthermore, the authors employed four ML approaches to categorize each review of FDAs into the respective sentiment class.FindingsAccording to the study's findings, the majority of customer reviews of FDAs were positive. This research also revealed that, while all of the methods (decision tree, linear support vector machine, random forest classifier and logistic regression) can appropriately classify the reviews into a sentiment category, support vector machines (SVM) beats the others in terms of model accuracy. The authors' study also showed that logistic regression provided the highest recall, F1 score and lowest Root Mean Square Error (RMSE) among the four ML models.Practical implicationsThe findings aid FDAs in determining customer review behavior. The study's findings could help food apps developers better understand how customers feel about the developers' products and services. The food apps developer can learn how to use ML techniques to better understand the users' behavior.Originality/valueThe current study uses ML methodologies to investigate and predict consumer attitude regarding FDAs.
当前研究的目标是使用不同的机器学习(ML)方法来检查和预测客户对食品配送应用程序(fda)的评论。使用Google Play Scraper,我们从Google Play商店收集了5家外卖服务提供商的数据。在清理评论之后,过滤后的文本被分类为具有消极、积极或中立的情绪,然后使用两种无监督情绪算法(AFINN和情感推理的价感知词典(VADER))对其进行评分。此外,作者采用了四种机器学习方法将fda的每个评论分类到各自的情感类中。调查结果根据这项研究的结果,大多数消费者对fda的评价都是积极的。本研究还发现,虽然所有方法(决策树、线性支持向量机、随机森林分类器和逻辑回归)都可以适当地将评论分类到情感类别,但支持向量机(SVM)在模型准确性方面优于其他方法。作者的研究还表明,在四种ML模型中,逻辑回归提供了最高的召回率,F1得分和最低的均方根误差(RMSE)。实际意义研究结果有助于fda确定顾客评论行为。这项研究的发现可以帮助食品应用程序开发者更好地了解消费者对开发者产品和服务的感受。食品应用开发者可以学习如何使用机器学习技术来更好地理解用户的行为。原创性/价值当前的研究使用ML方法来调查和预测消费者对fda的态度。