Comparison of neutrosophic approach to various deep learning models for sentiment analysis
作者：
Highlights：
• Architecture for combining neutrosophy and deep learning for sentiment analysis.
• Experimental analysis using LSTM, BERT, RoBERTa, ALBERT, MPNet, and Ensemble models.
• Feature extraction using intermediate layers of deep learning models.
• Using K-means and Cosine distance for calculating SVNS from generated features.
• SVNS as an alternative to Softmax for multi-label sentiment analysis problem.
摘要
•Architecture for combining neutrosophy and deep learning for sentiment analysis.•Experimental analysis using LSTM, BERT, RoBERTa, ALBERT, MPNet, and Ensemble models.•Feature extraction using intermediate layers of deep learning models.•Using K-means and Cosine distance for calculating SVNS from generated features.•SVNS as an alternative to Softmax for multi-label sentiment analysis problem.
论文关键词：Neutrosophy,Sentiment analysis,BiLSTM,ALBERT,RoBERTa,BERT,MPNet,Stacked ensemble
论文评审过程：Received 12 December 2020, Revised 4 March 2021, Accepted 17 April 2021, Available online 23 April 2021, Version of Record 26 April 2021.
论文官网地址：https://doi.org/10.1016/j.knosys.2021.107058
