Deep Learning for predicting neutralities in Offensive Language Identification Dataset

作者：

Highlights：

• Sentiment analysis model using deep learning, neutrosophy, and transfer learning.

• Analysing tweets as a combination of sentiments identifying neutralities.

• Quantifying tweets into Single Valued Neutrosophic Sets (SVNS) for OLID dataset.

• Experimental analysis using BiLSTM, BERT, RoBERTa, ALBERT, and MPNet.

• Gaussian Mixture Model and k-means clustering algorithm for SVNS calculation.

摘要

•Sentiment analysis model using deep learning, neutrosophy, and transfer learning.•Analysing tweets as a combination of sentiments identifying neutralities.•Quantifying tweets into Single Valued Neutrosophic Sets (SVNS) for OLID dataset.•Experimental analysis using BiLSTM, BERT, RoBERTa, ALBERT, and MPNet.•Gaussian Mixture Model and k-means clustering algorithm for SVNS calculation.

论文关键词：Neutrosophy,SVNS,Sentiment analysis,BiLSTM,BERT,ALBERT,RoBERTa,MPNet,OLID

论文评审过程：Received 11 August 2020, Revised 16 June 2021, Accepted 19 June 2021, Available online 24 July 2021, Version of Record 28 July 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115458