Suggestion Mining from Opinionated Text of Big Social Media Data

作     者：Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi 

作者机构：Department of Computer ScienceCollege of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer ScienceFaculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software EngineeringFaculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer SciencesInstitute of Space TechnologyIslamabadPakistan Department of Computer ScienceCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information TechnologyCollege of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3323-3338页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research is funded by Taif University  TURSP-2020/115 

主　　题：Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset 

摘      要：:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.