An Ensemble-Based Hotel Reviews System Using Naive Bayes Classifier

作     者：Joseph Bamidele Awotunde Sanjay Misra Vikash Katta Oluwafemi Charles Adebayo 

作者机构：Department of Computer ScienceFaculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Computer Science and CommunicationOtfold University CollegeHaldenNorway 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2023年第137卷第10期

页      面：131-154页

核心收录：

学科分类：0601[历史学-考古学] 1301[艺术学-艺术学理论] 1202[管理学-工商管理] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Sentiment analysis hotel reviews Naive Bayes algorithm consumer opinions web 2.0 machine learning 

摘      要：The task of classifying opinions conveyed in any form of text online is referred to as sentiment *** emergence of social media usage and its spread has given room for sentiment analysis in our daily *** media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various *** subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for sentiment *** rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them *** to a recent study,81%of consumers conduct online research prior to making a *** the reviews available online are too huge and numerous for human brains to process and ***,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so *** the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market ***,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the *** dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this *** performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.