Aspect-Based Sentiment Analysis for Polarity Estimation of Customer Reviews on Twitter

作     者：Ameen Banjar Zohair Ahmed Ali Daud Rabeeh Ayaz Abbasi Hussain Dawood 

作者机构：Department of Information Systems and TechnologyCollege of Computer Science and EngineeringUniversity of JeddahJeddah21589Saudi Arabia Air UniversityIslamabad44000Pakistan Department of Computer ScienceIslamabad44000Pakistan Department of Computer and Network EngineeringCollege of Computer Science and EngineeringUniversity of JeddahJeddah21589 Saudi Arabia 

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

年 卷 期：2021年第67卷第5期

页      面：2203-2225页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：funded by the University of Jeddah Saudi Arabia under Grant No.(UJ-12-18-DR) 

主　　题：Natural language processing sentiment analysis aspect co-occurrence calculation sentiment polarity customer reviews twitte 

摘      要：Most consumers read online reviews written by different users before making purchase decisions,where each opinion expresses some ***,sentiment analysis is currently a hot topic of *** particular,aspect-based sentiment analysis concerns the exploration of emotions,opinions and facts that are expressed by people,usually in the form of *** is crucial to consider polarity calculations and not simply categorize reviews as positive,negative,or ***,the available lexicon-based method accuracy is affected by limited *** of the available polarity estimation techniques are too general and may not reect the aspect/topic in question if reviews contain a wide range of information about different *** paper presents a model for the polarity estimation of customer reviews using aspect-based sentiment analysis(ABSA-PER).ABSA-PER has three major phases:data preprocessing,aspect co-occurrence calculation(CAC)and polarity estimation.A multi-domain sentiment dataset,Twitter dataset,and trust pilot forum dataset(developed by us by dened judgement rules)are used to verify *** outcomes show that ABSA-PER achieves better accuracy,i.e.,85.7%accuracy for aspect extraction and 86.5%accuracy in terms of polarity estimation,than that of the baseline methods.