Abstract
Morocco’s goal is to reach 52% of the energy mix by 2030. However, Moroccans’ acceptance and support for renewable energy are important to accelerate renewable deployment and increase the share of renewables in the total power capacity. To the best of our knowledge, this work is the first study in the Middle East and North Africa region that addresses people’s opinions on renewable energy using a natural language processing technique called sentiment analysis. The process started by collecting Moroccan’s comments on renewable energy during the past 10 years. After preprocessing, we identified the polarity (positive or negative) of each comment by applying one of the sentiment analysis approaches, namely a rule-based approach. As a result, Moroccans do not show a high positive sentiment toward renewables, which means there is a need for more public awareness. Further, Moroccans show more attention to wind and solar energy than other types of renewables because of the naturally available resources. In terms of energy policy, the public indicates that the Moroccan government focuses on big projects and does not encourage energy self-sufficiency. As a powerhouse in this domain, Morocco may consider educating and encouraging people to use renewables in order to win more support for clean energy, reduce its energy dependency, and help the growth of the renewables business.
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13 January 2024
A Correction to this paper has been published: https://doi.org/10.1007/s13278-023-01191-9
References
Abdar M et al (2020) Energy choices in Alaska: mining people’s perception and attitudes from geotagged tweets. Renew Sustain Energy Rev 124:109781. https://doi.org/10.1016/j.rser.2020.109781
Abdelali A, Darwish K, Durrani N, Mubarak H (2016) Farasa: a fast and furious segmenter for Arabic. In Proceedings of the 2016 conference of the north American chapter of the association for computational linguistics: demonstrations, Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 11–16. https://doi.org/10.18653/v1/N16-3003
Bach W (1979) Impact of increasing atmospheric CO2 concentrations on global climate: potential consequences and corrective measures. Environ Int 2(4–6):215–228. https://doi.org/10.1016/0160-4120(79)90004-7
Benali L (2019) Electricity-sector Reforms in the MENA Region. In: Perspectives on development in the middle east and north Africa (MENA) Region. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-96268-9
Benazzouz A, Mabchour H, El Had K, Zourarah B, Mordane S (2020) Offshore wind energy resource in the Kingdom of Morocco: assessment of the seasonal potential variability based on satellite data. J Mar Sci Eng 9(1):31. https://doi.org/10.3390/jmse9010031
Birjali M, Kasri M, Beni-Hssane A (2021) A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl Based Syst 226:107134. https://doi.org/10.1016/j.knosys.2021.107134
Bouhrim H, El Marjani A (2019) Wave energy assessment along the Moroccan Atlantic coast. J Mar Sci Appl 18(2):142–152. https://doi.org/10.1007/s11804-018-00060-8
Chai CP (2022) Comparison of text preprocessing methods. Nat Lang Eng, 1–45. https://doi.org/10.1017/S1351324922000213.
Chen N-H (2020) Exploring the cognitive and emotional impact of online climate change videos on viewers. Sustainability 12(22):9571. https://doi.org/10.3390/su12229571
Chen L et al (2022) Strategies to achieve a carbon neutral society: a review. Environ Chem Lett 20(4):2277–2310. https://doi.org/10.1007/s10311-022-01435-8
Chen Y, Yao J (2021) Sentiment analysis using part-of-speech-based feature extraction and game-theoretic rough sets. In 2021 International conference on data mining workshops (ICDMW), IEEE. pp 110–117. https://doi.org/10.1109/ICDMW53433.2021.00020
Corrêa KC, Uriona-Maldonado M, Vaz CR (2022) The evolution, consolidation and future challenges of wind energy in Uruguay. Energy Policy 161:112758. https://doi.org/10.1016/j.enpol.2021.112758
el Khchine Y, Sriti M, el Kadri Elyamani NE (2019) Evaluation of wind energy potential and trends in Morocco. Heliyon 5(6):e01830. https://doi.org/10.1016/j.heliyon.2019.e01830
Eshchanov B, Grinwis Plaat Stultjes M, Eshchanov R, Komilovich S (2011) People’s perceptions on renewable energy sources penetration prospects in the Khorezm Province, Uzbekistan. J Knowl Manag Econ Inf Technol, 1
Fagbola TM, Abayomi A, Mutanga MB, Jugoo V (2022) Lexicon-based sentiment analysis and emotion classification of climate change related tweets. pp 637–646. https://doi.org/10.1007/978-3-030-96302-6_60.
Fan B, Wu T, Zhuang Y, Peng J, Huang K (2021) The development of energy storage in China: policy evolution and public attitude. Front Energy Res 9:797478. https://doi.org/10.3389/fenrg.2021.797478
Fersini E (2017) Sentiment analysis in social networks. In Sentiment analysis in social networks, Elsevier, 2017, pp 91–111. https://doi.org/10.1016/B978-0-12-804412-4.00006-1.
Gielen D, Boshell F, Saygin D, Bazilian MD, Wagner N, Gorini R (2019) The role of renewable energy in the global energy transformation. Energy Strat Rev 24:38–50. https://doi.org/10.1016/j.esr.2019.01.006
Greenberg M (2009) Energy sources, public policy, and public preferences: analysis of US national and site-specific data. Energy Policy 37(8):3242–3249. https://doi.org/10.1016/j.enpol.2009.04.020
Hagen B, Pijawka D (2015) Public perceptions and support of renewable energy in North America in the context of global climate change. Int J Disaster Risk Sci 6(4):385–398. https://doi.org/10.1007/s13753-015-0068-z
Haidi T, Cheddadi B, El Mariami F, El Idrissi Z, Tarrak A (2021) Wind energy development in Morocco: evolution and impacts. Int J Electr Comput Eng (IJECE) 11(4):2811. https://doi.org/10.11591/ijece.v11i4.pp2811-2819
Hauthal E, Burghardt D, Fish C, Griffin AL (2020) Sentiment Analysis. In international encyclopedia of human geography, Elsevier, pp 169–177. https://doi.org/10.1016/B978-0-08-102295-5.10593-1.
Huijts NMA, Molin EJE, Steg L (2012) Psychological factors influencing sustainable energy technology acceptance: a review-based comprehensive framework. Renew Sustain Energy Rev 16(1):525–531. https://doi.org/10.1016/j.rser.2011.08.018
Hussain A, Cambria E (2018) Semi-supervised learning for big social data analysis. Neurocomputing 275:1662–1673. https://doi.org/10.1016/j.neucom.2017.10.010
Ibar-Alonso R, Quiroga-García R, Arenas-Parra M (2022) Opinion mining of green energy sentiment: a Russia-Ukraine conflict analysis. Mathematics 10(14):2532. https://doi.org/10.3390/math10142532
Jabalameli S, Xu Y, Shetty S (2022) Spatial and sentiment analysis of public opinion toward COVID-19 pandemic using twitter data: at the early stage of vaccination. Int J Dis Risk Reduct 80:103204. https://doi.org/10.1016/j.ijdrr.2022.103204
Jain A, Jain V (2019) Sentiment classification of twitter data belonging to renewable energy using machine learning. J Inf Optim Sci 40(2):521–533. https://doi.org/10.1080/02522667.2019.1582873
Jin Y (2017) Development of word cloud generator software based on Python. Procedia Eng 174:788–792. https://doi.org/10.1016/j.proeng.2017.01.223
Kaity M, Balakrishnan V (2020) Sentiment lexicons and non-English languages: a survey. Knowl Inf Syst 62(12):4445–4480. https://doi.org/10.1007/s10115-020-01497-6
Kasri M, Birjali M, Beni-Hssane A (2019) A comparison of features extraction methods for Arabic sentiment analysis. In Proceedings of the 4th International Conference on Big Data and Internet of Things, New York, NY, USA: ACM, pp 1–6. https://doi.org/10.1145/3372938.3372998.
Kim SY, Ganesan K, Dickens P, Panda S (2021) Public sentiment toward solar energy—opinion mining of twitter using a transformer-based language model. Sustainability 13(5):2673. https://doi.org/10.3390/su13052673
Kousksou T et al (2015) Renewable energy potential and national policy directions for sustainable development in Morocco. Renew Sustain Energy Rev 47:46–57. https://doi.org/10.1016/j.rser.2015.02.056
Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
Loureiro ML, Alló M (2020) Sensing climate change and energy issues: sentiment and emotion analysis with social media in the U.K. and Spain. Energy Policy 143:111490. https://doi.org/10.1016/j.enpol.2020.111490
Mana AA, Allouhi A, Ouazzani K, Jamil A (2021) Feasibility of agriculture biomass power generation in Morocco: Techno-economic analysis. J Clean Prod 295:126293. https://doi.org/10.1016/j.jclepro.2021.126293
Mouakkir L, El Hou M, Mordane S, Chagdali M (2022) Wave energy potential analysis in the Casablanca-Mohammedia Coastal Area (Morocco). J Mar Sci Appl 21(1):92–101. https://doi.org/10.1007/s11804-022-00261-2
Mukherjee P, Badr Y, Doppalapudi S, Srinivasan SM, Sangwan RS, Sharma R (2021) Effect of negation in sentences on sentiment analysis and polarity detection. Procedia Comput Sci 185:370–379. https://doi.org/10.1016/j.procs.2021.05.038
Nhlabano VV, Lutu PEN (2018) Impact of text pre-processing on the performance of sentiment analysis models for social media data. In 2018 International conference on advances in big data, computing and data communication systems (icABCD), IEEE, pp 1–6. https://doi.org/10.1109/ICABCD.2018.8465135
Nuortimo K, Härkönen J (2018) Opinion mining approach to study media-image of energy production. implications to public acceptance and market deployment. Renew Sustain Energy Rev 96:210–217. https://doi.org/10.1016/j.rser.2018.07.018
Nuortimo K, Härkönen J, Karvonen E (2018) Exploring the global media image of solar power. Renew Sustain Energy Rev 81:2806–2811. https://doi.org/10.1016/j.rser.2017.06.086
Poria S, Hazarika D, Majumder N, Mihalcea R (2020) Beneath the tip of the Iceberg: current challenges and new directions in sentiment analysis research
Pozzi Federico L, Fersini E, Messina E (2017) Sentiment analysis in social networks. Elsevier. https://doi.org/10.1016/C2015-0-01864-0
Pradha S, Halgamuge MN, Tran Quoc Vinh N (2019) Effective text data preprocessing technique for sentiment analysis in social media data. In 2019 11th international conference on knowledge and systems engineering (KSE), IEEE, 2019. 1–8. https://doi.org/10.1109/KSE.2019.8919368.
Qazi A.et al (2022) Analyzing the public opinion as a guide for renewable-energy status in Malaysia: a case study. In: IEEE Trans Eng Manag, pp 1–15. https://doi.org/10.1109/TEM.2020.3046749
Shaalan K, Siddiqui S, Alkhatib M, Abdel Monem A (2018) Challenges in Arabic natural language processing. In Computational linguistics, speech and image processing for arabic language, World Scientific, pp 59–83. https://doi.org/10.1142/9789813229396_0003
Sierra JP, Martín C, Mösso C, Mestres M, Jebbad R (2016) Wave energy potential along the Atlantic coast of Morocco. Renew Energy 96:20–32. https://doi.org/10.1016/j.renene.2016.04.071
Srinivasan R, Subalalitha CN (2021) Sentimental analysis from imbalanced code-mixed data using machine learning approaches. Distrib Parallel Databases. https://doi.org/10.1007/s10619-021-07331-4
Stern N, Valero A (2021) Innovation, growth and the transition to net-zero emissions. Res Policy 50(9):104293. https://doi.org/10.1016/j.respol.2021.104293
Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307. https://doi.org/10.1162/COLI_a_00049
Tachicart R, Bouzoubaa K (2021) Moroccan Arabic vocabulary generation using a rule-based approach. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.02.013
Tuitjer L, Dirksmeier P (2021) Social media and perceived climate change efficacy: a European comparison. Digit Geogr Soc 2:100018. https://doi.org/10.1016/j.diggeo.2021.100018
Vashishtha S, Susan S (2019) Fuzzy rule based unsupervised sentiment analysis from social media posts. Expert Syst Appl 138:112834. https://doi.org/10.1016/j.eswa.2019.112834
Vespa M, Kortsch T, Hildebrand J, Schweizer-Ries P, Volkmer SA (2022) Not all places are equal: using instagram to understand cognitions and affect towards renewable energy infrastructures. Sustainability 14(7):4071. https://doi.org/10.3390/su14074071
Wankhade M, Rao ACS, Kulkarni C (2022) A survey on sentiment analysis methods, applications, and challenges. Artif Intell Rev 55(7):5731–5780. https://doi.org/10.1007/s10462-022-10144-1
WorldPublicOpinion.net, “World Publics Strongly Favor Requiring More Wind and Solar Energy, More Efficiency, Even If It Increases Costs,” 2008
Wu F, Huang Y, Yuan Z (2017) Domain-specific sentiment classification via fusing sentiment knowledge from multiple sources. Inf Fusion 35:26–37. https://doi.org/10.1016/j.inffus.2016.09.001
Zarrabeitia-Bilbao E, Morales-i-Gras J, Río-Belver R-M, Garechana-Anacabe G (2022) Green energy: identifying development trends in society using Twitter data mining to make strategic decisions. El Prof De La Inf. https://doi.org/10.3145/epi.2022.ene.14
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MK and AEA carried out the experiment. MK wrote the manuscript with support from MEF. MB and BC helped supervise the project. MK and ABH conceived the original idea. ABH supervised the project.
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Kasri, M., El-Ansari, A., El Fissaoui, M. et al. Public sentiment toward renewable energy in Morocco: opinion mining using a rule-based approach. Soc. Netw. Anal. Min. 13, 124 (2023). https://doi.org/10.1007/s13278-023-01119-3
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DOI: https://doi.org/10.1007/s13278-023-01119-3