A Semi-Supervised Approach for Aspect Category Detection and Aspect Term Extraction from Opinionated Text

作     者：Bishrul Haq Sher Muhammad Daudpota Ali Shariq Imran Zenun Kastrati Waheed Noor 

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] 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.