A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets
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DOI: 10.1007/s10796-021-10135-7
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Cited by:
- Intan Nurma Yulita & Victor Wijaya & Rudi Rosadi & Indra Sarathan & Yusa Djuyandi & Anton Satria Prabuwono, 2023. "Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)," Data, MDPI, vol. 8(3), pages 1-17, February.
- Victor Chang & Carole Goble & Muthu Ramachandran & Lazarus Jegatha Deborah & Reinhold Behringer, 2021. "Editorial on Machine Learning, AI and Big Data Methods and Findings for COVID-19," Information Systems Frontiers, Springer, vol. 23(6), pages 1363-1367, December.
- Waseem Ahmad & Bang Wang & Philecia Martin & Minghua Xu & Han Xu, 2023. "Enhanced sentiment analysis regarding COVID-19 news from global channels," Journal of Computational Social Science, Springer, vol. 6(1), pages 19-57, April.
- Carlos Henríquez Miranda & German Sanchez-Torres & Dixon Salcedo, 2023. "Exploring the Evolution of Sentiment in Spanish Pandemic Tweets: A Data Analysis Based on a Fine-Tuned BERT Architecture," Data, MDPI, vol. 8(6), pages 1-18, May.
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Keywords
COVID-19; Sentiment analysis; Twitter; Recurrent neural network (RCN); Heterogeneous Euclidean overlap metric (H-EOM); Hybrid heterogeneous support vector machine (H-SVM);All these keywords.
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