A performant deep learning model for sentiment analysis of climate change

Climate change is one of the most trend topics of the decade in the world. The recent years were the warmest in 139 years, however identifying deniers and believers of this subject still a very big issue. The challenge is to have an efficient tool to detect deniers in order to deploy the appropriate...

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Bibliographic Details
Published in:Social network analysis and mining 2022-12, Vol.13 (1), p.8
Main Authors: Lydiri, Mustapha, El Mourabit, Yousef, El Habouz, Youssef, Fakir, Mohamed
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Bidirectionality
Classification
Climate change
Customer feedback
Data mining
Deep learning
Denialism
Dictionaries
Governance
Labeling
Life Sciences
Machine learning
Natural language processing
Neural networks
Public opinion
Regression models
Sentiment analysis
Smart cities
Social media
Social networks
Support vector machines
Text categorization
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Summary:Climate change is one of the most trend topics of the decade in the world. The recent years were the warmest in 139 years, however identifying deniers and believers of this subject still a very big issue. The challenge is to have an efficient tool to detect deniers in order to deploy the appropriate strategy facing this phenomenon. Moreover, Bidirectional Encoder Representations from Transformers (BERT) pre-trained model has taken Natural Language Processing tasks results so far. In this paper we presented an efficient technological tool based on deep learning model and BERT model for detecting people’s opinions on climate change on social media platforms. We used convolutional neural network targeting the public opinions on climate change on Twitter. The results showed that our model outperforms the machine learning approaches: Naive Bays, Support Vector Machine and Logistic Regression. This model is able to analyze people’s behavior and detect believers and deniers of this disaster with high accuracy results (98% for believers and 90% for deniers). Our model could be a powerful citizen sensing tool that can be used by governments for monitoring and governance, especially for smart cities.
ISSN:1869-5450
1869-5469
DOI:10.1007/s13278-022-01014-3