情绪分析
计算机科学
深度学习
特征提取
人工智能
特征工程
特征(语言学)
数据科学
舆论
信息抽取
情报检索
万维网
自然语言处理
语言学
哲学
政治
政治学
法学
作者
Ganpat Singh Chauhan,Ravi Nahta,Yogesh Kumar Meena,Dinesh Gopalani
标识
DOI:10.1016/j.cosrev.2023.100576
摘要
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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