A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets

作者：

Highlights：

• Proposed a new fusion model for sentiment analysis of tweets.

• Model is trained and validated using large-scaled Twitter dataset.

• Coronavirus-related tweets of people in 8 highly affected countries are studied.

• Meaningful patterns are observed in various affected countries and time intervals.

• It may help scientists and governments on providing urgent aids to affected areas.

摘要

•Proposed a new fusion model for sentiment analysis of tweets.•Model is trained and validated using large-scaled Twitter dataset.•Coronavirus-related tweets of people in 8 highly affected countries are studied.•Meaningful patterns are observed in various affected countries and time intervals.•It may help scientists and governments on providing urgent aids to affected areas.

论文关键词：Deep learning,Coronavirus (COVID-19),Sentiment analysis,Information fusion,Tweet analysis

论文评审过程：Received 7 June 2020, Revised 30 April 2021, Accepted 15 June 2021, Available online 25 June 2021, Version of Record 29 June 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.107242