PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

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  Maada, L., Al Fararni, K., Aghoutane, B., Fattah, M., & Farhaoui, Y. (2022). A comparative study of sentiment analysis machine learning approaches. In 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) (pp. 1-5). IEEE. https://doi.org/10.1109/IRASET52964.2022.9738346
  
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  Mustapha, W. N. A. W., Sabri, N. M., Abu Bakar, N. A. A., Nik Daud, N. M., & Azizan, A. (2024). Detection of harassment toward women in Twitter during pandemic based on machine learning. International Journal of Advanced Computer Science and Applications, 15(3), 1035-1043. https://doi.org/10.14569/IJACSA.2024.01503103
  
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  Otero, P., Gago, J., & Quintas, P. (2021). Twitter data analysis to assess the interest of citizens on the impact of marine plastic pollution. Marine Pollution Bulletin, 170. https://doi.org/10.1016/j.marpolbul.2021.112620
  
  Pai, A. (2022, June 21). What is Tokenization in NLP? Here’s all you need to know. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2020/05/what-is-tokenization-nlp/
  
  Ram, N. R., Gautum, S., Jadeja, A., Joisar, H., & Rathore, N. (2024). Social media sentiment analysis using Twitter dataset. In 2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU) (pp. 1-5). IEEE. https://doi.org/10.1109/IC-CGU58078.2024.10530694
  
  Ramanathan, V., Al Hajri, H., & Ruth, A. (2024). Conceptual level semantic sentiment analysis using Twitter data. In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) (pp. 1-8). IEEE. https://doi.org/10.1109/ADICS58448.2024.10533498
  
  Ray, S., & Kumar, A. M. S. (2023). Prediction and analysis of sentiments of reddit users towards the climate change crisis. In 2023 International Conference on Networking and Communications (ICNWC) (pp. 1-7). IEEE. https://doi.org/10.1109/ICNWC57852.2023.10127496.
  
  Reddy, M. B. K., Vani, B., & Babu, C. N. K. (2022). A comparative analysis for the detection of hit rate of popular music videos in social network using logistic regression over support vector machine algorithm. In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) (pp. 544-549). IEEE. https://doi.org/10.1109/ICIEM54221.2022.9853055
  
  Ruz, G. A., Henríquez, P. A., & Mascareño, A. (2020). Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers. Future Generation Computer Systems, 106, 92-104. https://doi.org/10.1016/j.future.2020.01.005
  
  Shofiya, C., & Abidi, S. (2021). Sentiment analysis on COVID-19-related social distancing in Canada using twitter data. International Journal of Environmental Research and Public Health, 18(11), 5993. https://doi.org/10.3390/ijerph18115993
  
  Singh, G., Singh, J., & Tripathy, B. (2024). Significance of sentiment analysis approaches using Machine Learning (ML) techniques. In 2024 2nd International Conference on Computer, Communication and Control (IC4) (pp. 1-6). IEEE. https://doi.org/10.1109/IC457434.2024.10486310
  
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  Sudhir, P., & Suresh, V. D. (2021). Comparative study of various approaches, applications and classifiers for sentiment analysis. Global Transitions Proceedings, 2(36), 205-211. https://doi.org/10.1016/j.gltp.2021.08.004
  
  Thenmozhi, M., Shubigsha, G., Sindhuja, G., & Dhinakar, V. (2024). Sentiment analysis on climate change using Twitter data. In 2024 2nd International Conference on Networking and Communications (ICNWC) (pp. 1-6). IEEE. https://doi.org/10.1109/icnwc60771.2024.10537404
  
  Varshney, H., Shishodiya, P., & Sriramulu, S. (2022). Classifying tweets based on climate change. In 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) (pp. 956-960). IEEE. https://doi.org/10.1109/ICIEM54221.2022.9853094.
  
  Wali, K. (2022, May 4). Explained: Stemming vs lemmatization in NLP. Analytics India Magazine. https://analyticsindiamag.com/explained-stemming-vs-lemmatization-in-nlp
  
  Wang, J., Obradovich, N., & Zheng, S. (2020). A 43-million-person investigation into weather and expressed sentiment in a changing climate. One Earth, 2(6), 568-577. https://doi.org/10.1016/j.oneear.2020.05.016
  
  Yogi, K. S., Gowda, V. D., Sindhu, D., Soni, H., Mukherjee, S., & Madhu, G. C. (2024). Enhancing accuracy in social media sentiment analysis through comparative studies using machine learning techniques. In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) (Vol. 1, pp. 1-6). IEEE. https://doi.org/10.1109/ICKECS61492.2024.10616441