A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

作     者：Muhammad Aasim Qureshi Muhammad Asif Mohd Fadzil Hassan Ghulam Mustafa Muhammad Khurram Ehsan Aasim Ali Unaza Sajid 

作者机构：Department of Computer SciencesBahria UniversityLahore Campus54000Pakistan Computer and Information Science DepartmentUniversity TeknologiPetronas32610Malaysia 

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

年 卷 期：2022年第70卷第3期

页      面：4987-5004页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：National Institutes of Health(R35 GM118069) 

主　　题：Machine learning natural language processing annotation semi-annotated technique reviews annotation text annotation corpus annotation 

摘      要：In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the *** sentiment analysis,annotated data is a basic ***,this data is manually *** annotation is time consuming,costly and laborious *** overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment *** is created from the reviews of ten most popular songs on *** of five aspects—voice,video,music,lyrics and song,are *** N-Gram based technique is *** dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated *** the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed ***’s Kappa statistics is used to evaluate the degree of agreement between the two *** high Kappa value(i.e.,0.9571%)shows the high level of agreement between the *** validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational *** research also contributes in consolidating the guidelines for the manual annotation process.