使用深度学习方法的面向情感分析:一项调查

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Computer Science Review Pub Date : 2023-08-01 DOI:10.1016/j.cosrev.2023.100576
Ganpat Singh Chauhan , Ravi Nahta , Yogesh Kumar Meena , Dinesh Gopalani
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引用次数: 2

摘要

在线门户网站上丰富的非结构化文本使意见挖掘成为研究人员、学者和企业提取信息以收集、分析和聚合人类情感的最热门领域。从文本中提取公众情绪的一个方面对市场上的各种业务做出了非凡的贡献。近年来,基于深度学习的技术在没有高级特征工程的情况下学习了高级语言特征。因此,本文重点研究了基于深度学习的基于方面的情感分析(ABSA)方法的两个子任务,即方面提取和方面类别检测。通过对最先进和最新的方面提取方法的全面评估,证明了ABSA领域的重大进展。
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Aspect based sentiment analysis using deep learning approaches: A survey

The wealth of unstructured text on the online web portal has made opinion mining the most thrust area for researchers, academicians, and businesses to extract information for gathering, analyzing, and aggregating human emotions. The extraction of public sentiment from the text at an aspect level has contributed exceptionally to various businesses in the marketplace. In recent times, deep learning-based techniques have learned high-level linguistic features without high-level feature engineering. Therefore, this paper focuses on a rigorous survey on two primary subtasks, aspect extraction and aspect category detection of aspect-based sentiment analysis (ABSA) methods based on deep learning. The significant advancement in the ABSA sector is demonstrated by a thorough evaluation of state-of-the-art and latest aspect extraction methodologies.

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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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