文章基本信息

标题：ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System
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作者：Maganti Syamala ; N.J.Nalini
期刊名称：TEM Journal
印刷版ISSN：2217-8309
电子版ISSN：2217-8333
出版年度：2021
卷号：10
期号：1
页码：82-94
DOI：10.18421/TEM101-11
语种：English
出版社：UIKTEN
摘要：Aspect based sentient analysis (ABSA) is identified as one of the current research problems in Natural Language Processing (NLP). Traditional ABSA requires manual aspect assignment for aspect extraction and sentiment analysis. In this paper, to automate the process, a domain-independent dynamic ABSA model by the fusion of Efficient Named Entity Recognition (E-NER) guided dependency parsing technique with Neural Networks (NN) is proposed. The extracted aspects and sentiment terms by E-NER are trained to a Convolutional Neural Network (CNN) using Word embedding’s technique. Aspect categorybased polarity prediction is evaluated using NLTK Vader Sentiment package. The proposed model was compared to traditional rule-based approach, and the proposed dynamic model proved to yield better results by 17% when validated in terms of correctly classified instances, accuracy, precision, recall and F-Score using machine learning algorithms.
关键词：Aspect;Category;Extraction;Dependency Parsing;Domain-Independent;Dynamic;Named Entity Recognition;Polarity;Prediction;Sentiment