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A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

Computers, Materials & Continua 2022年第3期70卷 4987-5004页

作者： Muhammad Aasim Qureshi Muhammad Asif Mohd Fadzil Hassan Ghulam Mustafa Muhammad Khurram Ehsan Aasim Ali Unaza Sajid Department of Computer Sciences Bahria UniversityLahore Campus54000Pakistan Computer and Information Science Department University TeknologiPetronas32610Malaysia

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.

关键词： Machine learning natural language processing annotation semi-annotated technique reviews annotation text annotation corpus annotation

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