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Predictive intelligence in harmful news identification by BERT-based ensemble learning model with text sentiment analysis
Szu Yin Lin
*
, Yun Ching Kung, Fang Yie Leu
*
此作品的通信作者
管理科學系
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引文 斯高帕斯(Scopus)
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Keyphrases
Transformer-based
100%
Ensemble Learning Model
100%
Text Sentiment Analysis
100%
Bidirectional Encoder Representations from Transformers
100%
News Identification
100%
Predictive Intelligence
100%
Text Sentiment
100%
Transformer Model
66%
Term Frequency-inverse Document Frequency (TF-IDF)
33%
Frequency Approach
33%
Development Model
33%
Support Vector Machine Model
33%
News Content
33%
News Reporting
33%
News Articles
33%
Media Framing
33%
Reader Perceptions
33%
Ensemble Learning Method
33%
Ensemble Learning Techniques
33%
F1 Score
33%
News Text
33%
Fake Information
33%
Public Arena
33%
Personal Viewpoint
33%
Dangerous Speech
33%
Personal Emotion
33%
Lagrangian Support Vector Machines
33%
Information Disorder
33%
Harmful Information
33%
Computer Science
Sentiment Analysis
100%
Ensemble Learning
100%
Bidirectional Encoder Representations From Transformers
100%
Experimental Result
33%
Inverse Document Frequency
33%
Support Vector Machine
33%
Learning Technique
33%
Frequency Approach
33%
Development Model
33%
Mathematics
Opinion Mining
100%
Support Vector Machine
50%
Frequency Approach
50%
Lagrangian
50%
Inverse Document Frequency
50%
Termfrequency
50%
Chemical Engineering
Support Vector Machine
100%