Climate Change Sentiment Analysis Using Domain Specific Bidirectional Encoder Representations From Transformers-Reference-Cited by-同舟云学术

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Climate Change Sentiment Analysis Using Domain Specific Bidirectional Encoder Representations From Transformers

Published:2024 Issue: Volume:12 Page:114912-114922
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Anoop V. S.¹^ORCID,Krishnan T. K. Ajay¹,Daud Ali²^ORCID,Banjar Ameen³^ORCID,Bukhari Amal³^ORCID

Affiliation:

1. NLP for Social Good Laboratory, School of Digital Sciences, Kerala University of Digital Sciences, Innovation and Technology, Thiruvananthapuram, India

2. Faculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab Emirates

3. Department of Information Systems and Technology, College of Computer Science and Engineering (CCSE), University of Jeddah, Jeddah, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10632142.pdf?arnumber=10632142

Reference48 articles.

1. Deep Learning in Sentiment Analysis: Recent Architectures

2. Improving drought mitigation strategies and disaster risk reduction through MODIS and TRMM-based data in relation to climate change over Pakistan

3. Aspect-based sentiment analysis using smart government review data

4. Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model

5. Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions

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