A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text
作者机构:Department of Computer ScienceSukkur IBA UniversitySukkur65200Pakistan Department of Computer ScienceNorwegian University of Science and Technology(NTNU)Gjøvik2815Norway Department of InformaticsLinnaeus UniversityVäxjö35195Sweden Department of Computer Science&ITUniversity of BalochistanQuetta87300Pakistan
出 版 物:《Computers, Materials & Continua》 (计算机、材料和连续体(英文))
年 卷 期:2023年第77卷第10期
页 面:115-137页
核心收录:
学科分类:081203[工学-计算机应用技术] 08[工学] 0805[工学-材料科学与工程(可授工学、理学学位)] 0835[工学-软件工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:Natural language processing sentiment analysis aspect-based sentiment analysis topic-modeling POS tagging zero-shot learning
摘 要:The Internet has become one of the significant sources for sharing information and expressing users’opinions about products and their interests with the associated *** is essential to learn about product reviews;however,to react to such reviews,extracting aspects of the entity to which these reviews belong is equally ***-based Sentiment Analysis(ABSA)refers to aspects extracted from an opinionated *** literature proposes different approaches for ABSA;however,most research is focused on supervised approaches,which require labeled datasets with manual sentiment polarity labeling and aspect *** study proposes a semisupervised approach with minimal human supervision to extract aspect terms by detecting the aspect ***,the study deals with two main sub-tasks in ABSA,named Aspect Category Detection(ACD)and Aspect Term Extraction(ATE).In the first sub-task,aspects categories are extracted using topic modeling and filtered by an oracle further,and it is fed to zero-shot learning as the prompts and the augmented *** predicted categories are the input to find similar phrases curated with extracting meaningful phrases(e.g.,Nouns,Proper Nouns,NER(Named Entity Recognition)entities)to detect the aspect *** study sets a baseline accuracy for two main sub-tasks in ABSA on the Multi-Aspect Multi-Sentiment(MAMS)dataset along with SemEval-2014 Task 4 subtask 1 to show that the proposed approach helps detect aspect terms via aspect categories.