Mining Netizen's Opinion on Sustainable Agriculture: Sentiment Analysis of Twitter Data
DOI:
https://doi.org/10.58968/bs.v2i1.323Keywords:
Sustainable agriculture, Sentiment, TwitterAbstract
This research aims to measure public sentiment related to sustainable agriculture on the Twitter social media platform. The research method involves the extraction and classification of tweet data using a Python Library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The research utilized tweet data posted in the past one year. The results showed fluctuations and decreases in the number of tweets discussing sustainable agriculture. The location with the most tweet activity around sustainable agriculture was Brussels, Belgium, with 642 tweets during the observation period. Word cloud analysis on keywords showed that in positive sentiments, words such as "food security" and "climate change" dominated the visualization. On the other hand, in negative sentiments, words such as "farmer" and "private farmland" appeared more frequently. Overall, the majority of tweets expressed a positive attitude towards sustainable agriculture, with 68.5% positive sentiment. A total of 22.3% of tweets showed neutral sentiments, with no strong positive or negative tendencies. Only 9.1% of tweets contained negative sentiment, indicating that a small proportion of tweets expressed less favorable views towards sustainable agriculture.
References
Alamoodi, Zaidan, Zaidan, Albahri, Mohammed, Malik, Almahdi, Chyad, Tareq, Albahri, Hameed, H., & Alaad, M. (2021). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert Systems With Applications, January. https://doi.org/https://doi.org/10.1016/j.eswa.2020.114155
Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15-21.
FAO. (2021). Farm data management, sharing and services for agriculture development. https://doi.org/https://doi.org/10.4060/ cb2840en
FAO. (2022). World Food and Agriculture - Statistical Yearbook 2022. In Statistical Yearbook: World Food and Agriculture 2022. https://doi.org/10.4060/cc2211en
Firmansyah, I. (2022). A Sentiment Analysis on Profit Management. Management and Sustainability, 1(1).
Gomiero, T., Pimentel, D., & Paoletti1, M. G. (2011). Is There a Need for a More Sustainable Agriculture? Critical Reviews in Plant Sciences, 30(1), 6-23.
Hakim, B. A. H., Mujahidah, A. S., & Rusydiana, A. S. (2022). Sentiment Analysis on Halal Certification. Harmoni, 21(1), 78-93. https://doi.org/10.32488/harmoni.v21i1.609
Hassan, M. K., Hudaefi, F. A., & Caraka, R. E. (2022). Mining netizen's opinion on cryptocurrency: sentiment analysis of Twitter data. Studies in Economics and Finance, 39(3), 365-385. https://doi.org/10.1108/SEF-06-2021-0237
Lewandowski, I., Härdtlein, M., & Kaltschmitt, M. (1999). Sustainable crop production: Definition and methodological approach for assessing and implementing sustainability. Crop Science, 39(1), 184-193. https://doi.org/10.2135/cropsci1999.0011183X003900010029x
Liu, B. (2012). Sentiment Analysis and Mining of Opinions. Morgan & Claypool Publishers, 30(May), 503-523. https://doi.org/10.1007/978-3-319-60435-0_20
Maliha, H. (2023). Productive Zakat: An Intertemporal Sentiment Analysis. Islamic Economics and History, 2(1).
Maulida, S. (2022). A Sentiment Analysis on Pesantren Entrepreneurship. The Economic Review of Pesantren, 1(1).
Maulida, S., & Hakim, B. A. (2022). Twitter Sentiment Analysis on Green Finance. 1(1). http://ipublishing.intimal.edu.my/jods.html
Maulida, S., & Rusydiana, A. S. (2022). Twitter Sentiment Analysis on Credit Card. Maqasid al-Shariah Review, 1(1).
Mushi, G. E., Serugendo, G. D. M., & Burgi, P. Y. (2022). Digital Technology and Services for Sustainable Agriculture in Tanzania: A Literature Review. Sustainability (Switzerland), 14(4), 1-17. https://doi.org/10.3390/su14042415
Nuraini, I. (2022). Sentiment Analysis of Literature on Sharia Credit Card. Fara'id and Wealth Management, 2(1).
Ocktavia, A. K., Aziza, A. N., & Ikhwan, I. (2023). Digital Zakat: An Analysis of Twitter Sentiment. Islamic Marketing Review, 2(1).
Paoletti, M. G., Gomiero, T., & Pimentel, D. (2011). Introduction to the Special Issue: Towards A More Sustainable Agriculture. Critical Reviews in Plant Sciences, 30(1-2), 1. https://doi.org/10.1080/07352689.2011.553147
Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. In Knowledge-Based Systems (Vol. 89, Issue June 2015). https://doi.org/10.1016/j.knosys.2015.06.015
Reganold, J. P., Papendick, R. I., & Parr, J. F. (1990). Sustainable agriculture. Scientific American, 262(6), 112-120. https://doi.org/10.1038/scientificamerican0690-112
Riani, R., & Rusydiana, A. S. (2023). Exploring Sentiment Analysis of Sustainable Finance Initiatives: A Text Mining Approach. Accounting and Sustainability, 2(1).
Roe, C., Lowe, M., Williams, B., & Miller, C. (2021). Public perception of SARS-CoV-2 vaccinations on social media: Questionnaire and sentiment analysis. International Journal of Environmental Research and Public Health, 18(24). https://doi.org/10.3390/ijerph182413028
Rusydiana, A. S., & Marlina, L. (2020). Analisis sentimen terkait sertifikasi halal. Journal of Economics and Business Aseanomics, 5(1), 69-85.
Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2). https://doi.org/10.3390/agronomy10020207
Sarker, M. N. I., Islam, M. S., Ali, M. A., Islam, M. S., Salam, M. A., & Mahmud, S. M. H. (2019). Promoting digital agriculture through big data for sustainable farm management. International Journal of Innovation and Applied Studies, 25(4), 1235-1240. http://www.ijias.issr-journals.org/
Sauvenier, X., Valckx, J., Van Cauwenbergh, N., Wauters, E., Bachev, H., Biala, K., Bielders, C., Brouckaert, V., Garcia-Cidad, V., Goyens, S., Hermy, M., Mathijs, E., Muys, B., Vanclooster, M., & Peeters, A. (2005). Framework for assessing sustainability levels in Belgium agricultural systems - SAFE. Belgium Science Poilicy, 1(1), 1-125. https://mpra.ub.uni-muenchen.de/99616/
Tait, J., & Morris, D. (2000). Sustainable development of agricultural systems: Competing objectives and critical limits. Futures, 32(3-4), 247-260. https://doi.org/10.1016/S0016-3287(99)00095-6
Vargo, C. J., Guo, L., Mccombs, M., & Shaw, D. L. (2014). Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election. Journal of Communication, 64(2), 296-316. https://doi.org/10.1111/jcom.12089
Velten, S., Leventon, J., Jager, N., & Newig, J. (2015). What is sustainable agriculture? A systematic review. Sustainability (Switzerland), 7(6), 7833-7865. https://doi.org/10.3390/su7067833
Vinodhini, G., & Chandrasekaran, R. (2012). Sentiment Analysis and Opinion Mining: A Survey International Journal of Advanced Research in Sentiment Analysis and Opinion Mining: A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), 283-292.
Wang, D., Saleh, N. B., Byro, A., Zepp, R., Demessie, E. S.-, Luxton, T. P., Ho, K. T., Burgess, R. M., Flury, M., White, J. C., & Su, C. (2022). Nano-enabled pesticides for sustainable agriculture and global food security. In Nat Nanotechnol (Vol. 17, Issue 4). https://doi.org/10.1038/s41565-022-01082-8
