Zhaoliang Wu¹, Yuewei Wu^1,2, Xiaoli Feng¹, Jiajun Zou³, Fulian Yin^1,2,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3391-3412, 2024, DOI:10.32604/cmc.2024.047076

Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental area of research in Natural Language Processing (NLP). Within ABSA, Aspect Sentiment Quad Prediction (ASQP) aims to accurately identify sentiment quadruplets in target sentences, including aspect terms, aspect categories, corresponding opinion terms, and sentiment polarity. However, most existing research has focused on English datasets. Consequently, while ASQP has seen significant progress in English, the Chinese ASQP task has remained relatively stagnant. Drawing inspiration from methods applied to English ASQP, we propose Chinese generation templates and employ prompt-based instruction learning to enhance the model’s understanding of the task, ultimately improving ASQP performance in the… More >

Zhaoliang Wu¹, Shanyu Tang², Xiaoli Feng¹, Jiajun Zou³, Fulian Yin^1,4,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2873-2888, 2023, DOI:10.32604/cmc.2023.037060

Abstract Aspect-Based Sentiment Analysis (ABSA) is one of the essential research in the field of Natural Language Processing (NLP), of which Aspect Sentiment Quad Prediction (ASQP) is a novel and complete subtask. ASQP aims to accurately recognize the sentiment quad in the target sentence, which includes the aspect term, the aspect category, the corresponding opinion term, and the sentiment polarity of opinion. Nevertheless, existing approaches lack knowledge of the sentence’s syntax, so despite recent innovations in ASQP, it is poor for complex cyber comment processing. Also, most research has focused on processing English text, and ASQP for Chinese text is almost… More >