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Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches
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In: Sustainability; Volume 14; Issue 8; Pages: 4723 (2022)
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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.
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Keyword:
climate change; lexicon; machine learning; sentiment analysis; social media
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URL: https://doi.org/10.3390/su14084723
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22 |
Artificial Intelligent in Education
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In: Sustainability; Volume 14; Issue 5; Pages: 2862 (2022)
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23 |
eHealth Engagement on Facebook during COVID-19: Simplistic Computational Data Analysis
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 8; Pages: 4615 (2022)
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24 |
How Do Chinese People View Cyberbullying? A Text Analysis Based on Social Media
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In: International Journal of Environmental Research and Public Health; Volume 19; Issue 3; Pages: 1822 (2022)
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25 |
Knowledge Discovery from Large Amounts of Social Media Data
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1209 (2022)
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26 |
Detecting Depression Signs on Social Media: A Systematic Literature Review
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In: Healthcare; Volume 10; Issue 2; Pages: 291 (2022)
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27 |
A Novel Method of Generating Geospatial Intelligence from Social Media Posts of Political Leaders
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In: Information; Volume 13; Issue 3; Pages: 120 (2022)
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28 |
Vedantic Basis and Praxis of the Integral Advaita of Sri Aurobindo
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In: Monsoon: South Asian Studies Association Journal (2022)
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29 |
Tusha Hiti: The Origin and Significance of the Name
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In: Monsoon: South Asian Studies Association Journal (2022)
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30 |
Diversity, Equity, and Inclusion: Perspectives from Contemporary India and 6th Century Jain Yoga
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In: Monsoon: South Asian Studies Association Journal (2022)
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31 |
Ganges in Indian Sculpture and Literature: Mythology and Personification
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In: Monsoon: South Asian Studies Association Journal (2022)
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32 |
Digital and Spatial Humanities Mapping: Eurasia-Pacific Early Trade and Belief Linkages
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In: Monsoon: South Asian Studies Association Journal (2022)
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33 |
Overview of GermEval Task 2, 2019 shared task on the identification of offensive language
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34 |
Using social media and personality traits to assess software developers' emotions ...
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35 |
Using social media and personality traits to assess software developers' emotions ...
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36 |
Exploring the Effects of Linguistic Elements of Social Media Corporate Apologies on Consumer Responses
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In: Association of Marketing Theory and Practice Proceedings 2022 (2022)
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37 |
The “Social” in Social VR: A Linguistic Analysis of Verbal Behavior in Groups ...
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