.png){kind=icon}
返回
Enhanced sentimental analysis using visual geometry group network-based deep learning approach
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-021-05890-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this digital era, there are many reasons to investigate the positive and negative opinion: a lot of irrelevant or error-related information exists, and major changes can strengthen or undermine past knowledge due to the newly discovered information. Speculation and negation principles are used as a significant influence in determining the factuality of occurrences or words. Negation reverses the validity of argument to maximize or decrease the doubt regarding the opponent and conjecture. Deep learning recently demonstrated has increased ability to distinguish truthful and non-true knowledge. Moreover, different languages were used in most of the previous approaches. To our understanding, the negative or hypothetical concept in biomedical texts in English is not known in previous work. This work will establish an adaptive visual geometry group network (VGG Net) based on CNN models for the identification and rejection of statements (negated or speculated sentences, etc.). The applied models are tested by considering the English corpus BioScope. Finally, as a result, the statistical study confirms that the performance of the proposed models surpasses our original baseline on F1-Score.
Keyword:
Data mining
Opinion mining
English corpus bioscope
Adaptive visual geometry group network (VGG net) classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏分享收藏A survey on classification techniques for opinion mining and sentiment analysis观点挖掘和情感分析的分类技术综述
PREAI
分享收藏Detecting negation and scope in Chinese clinical notes using character and word embedding使用字符和单词嵌入检测中文临床笔记中的否定和范围
PREAI
分享收藏
查看更多内容