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Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches

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  • Nabila Mohamad Sham

    (Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Malaysia)

  • Azlinah Mohamed

    (Institute for Big Data Analytics and Artificial Intelligence, Universiti Teknologi MARA (UiTM), Shah Alam 40450, Malaysia)

Abstract

The emissions of greenhouse gases, such as carbon dioxide, into the biosphere have the consequence of warming up the planet, hence the existence of climate change. Sentiment analysis has been a popular subject and there has been a plethora of research conducted in this area in recent decades, typically on social media platforms such as Twitter, due to the proliferation of data generated today during discussions on climate change. However, there is not much research on the performances of different sentiment analysis approaches using lexicon, machine learning and hybrid methods, particularly within this domain-specific sentiment. This study aims to find the most effective sentiment analysis approach for climate change tweets and related domains by performing a comparative evaluation of various sentiment analysis approaches. In this context, seven lexicon-based approaches were used, namely SentiWordNet, TextBlob, VADER, SentiStrength, Hu and Liu, MPQA, and WKWSCI. Meanwhile, three machine learning classifiers were used, namely Support Vector Machine, Naïve Bayes, and Logistic Regression, by using two feature extraction techniques, which were Bag-of-Words and TF–IDF. Next, the hybridization between lexicon-based and machine learning-based approaches was performed. The results indicate that the hybrid method outperformed the other two approaches, with hybrid TextBlob and Logistic Regression achieving an F1-score of 75.3%; thus, this has been chosen as the most effective approach. This study also found that lemmatization improved the accuracy of machine learning and hybrid approaches by 1.6%. Meanwhile, the TF–IDF feature extraction technique was slightly better than BoW by increasing the accuracy of the Logistic Regression classifier by 0.6%. However, TF–IDF and BoW had an identical effect on SVM and NB. Future works will include investigating the suitability of deep learning approaches toward this domain-specific sentiment on social media platforms.

Suggested Citation

  • Nabila Mohamad Sham & Azlinah Mohamed, 2022. "Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches," Sustainability, MDPI, vol. 14(8), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4723-:d:794214
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    References listed on IDEAS

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    1. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    2. Mike Thelwall & Kevan Buckley & Georgios Paltoglou & Di Cai & Arvid Kappas, 2010. "Sentiment strength detection in short informal text," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2544-2558, December.
    3. Ning Xiang & Limao Wang & Shuai Zhong & Chen Zheng & Bo Wang & Qiushi Qu, 2021. "How Does the World View China’s Carbon Policy? A Sentiment Analysis on Twitter Data," Energies, MDPI, vol. 14(22), pages 1-17, November.
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    1. Ján Mojžiš & Peter Krammer & Marcel Kvassay & Lenka Skovajsová & Ladislav Hluchý, 2022. "Towards Reliable Baselines for Document-Level Sentiment Analysis in the Czech and Slovak Languages," Future Internet, MDPI, vol. 14(10), pages 1-23, October.
    2. Eva L. Jenkins & Dickson Lukose & Linda Brennan & Annika Molenaar & Tracy A. McCaffrey, 2023. "Exploring Food Waste Conversations on Social Media: A Sentiment, Emotion, and Topic Analysis of Twitter Data," Sustainability, MDPI, vol. 15(18), pages 1-26, September.

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