Sentiment Analysis of Comments Posted on Youtube Videos Related to Photovoltaics

• Autorzy: Marcin Wyskwarski
• Języki publikacji: EN

Abstrakty

EN

Purpose: Based on sentiment analysis of comments posted on YouTube, determining people's thoughts, feelings and opinions on photovoltaics. Design/methodology/approach: Comments posted on videos were downloaded automatically. The comments' content has undergone preprocessing. All characters other than letters, URLs, hashtags, emojis, and words used to search for videos taken out of their text. The comment's sentiment value was determined. To display the proportion of favourable, negative, and neutral comments, visualisations were created. The word cloud was employed to display the comments' most popular words. Findings: For comments posted on videos related to photovoltaics, proportions of positive, negative and neutral comments were determined. The information about the number of published videos, the view count of videos, the length of videos, the number of published comments, and the length of comments has been obtained. Research limitations/implications: Only comments posted on videos which contained the word "photovoltaic" were downloaded, Only Polish-language comments' content was examined. Without author oversight, sentiment analysis was carried out automatically by the "ccl emo" service. Only viewpoints expressed by YouTube users were analysed. It was assumed that if the title of the video contains the word photovoltaic, its comments content is related to photovoltaic. Practical implications: Automated assessment of people's opinions regarding photovoltaics. Originality/value: Opinions on photovoltaics were collected. Based on the growing number of videos and comments, it was found that interest in photovoltaics in Poland is steadily growing. (original abstract)

Słowa kluczowe

EN

Sentiment analysis; Photovoltaic systems; Text mining; Website; Social media; Video

PL

Analiza wydźwięku; Systemy fotowoltaiczne; Eksploracja tekstu; Serwis internetowy; Media społecznościowe; Wideo

• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2023
• Numer: z. 185 W kierunku przyszłości zarządzania = Towards Future of Management
• Strony: 619--637

Twórcy

autor

Marcin Wyskwarski

• Silesian University of Technology

Bibliografia

• 1. Ağrali, Ö., Aydin, Ö. (2021). Tweet Classification and Sentiment Analysis on Metaverse Related Messages. Journal of Metaverse, 1(1), 25-30.
• 2. Alves dos Santos, S.A., João, J.P., Carlos, C.A., Marques Lameirinhas, R.A. (2021). The impact of aging of solar cells on the performance of photovoltaic panels. Energy Conversion and Management: X, 10, 100082. https://doi.org/10.1016/ J.ECMX.2021.100082
• 3. Aydın, E., Yılmaz, E. (2021). YouTube as a Source of Information on Echocardiography: Content and Quality Analysis. Acta Cardiologica Sinica, 37(5), 534-541. https://doi.org/10.6515/ACS.202109_37(5).20210514A
• 4. Bhuiyan, M.A., An, J., Mikhaylov, A., Moiseev, N., Danish, M.S.S. (2021). Renewable Energy Deployment and COVID-19 Measures for Sustainable Development. Sustainability, Vol. 13(8), 4418. https://doi.org/10.3390/SU13084418
• 5. Bilgili, F., Ozturk, I. (2015). Biomass energy and economic growth nexus in G7 countries: Evidence from dynamic panel data. Renewable and Sustainable Energy Reviews, 49, 132-138. https://doi.org/10.1016/J.RSER.2015.04.098
• 6. Bórawski, P., Yashchenko, T., Sviderskyi, A., Dunn, J.W. (2019). Development of renewable energy market in the EU with particular regard to solar energy. Conference Proceedings. Determinants Of Regional Development, 1, 12-13. https://doi.org/10.14595/CP/01/003
• 7. Marques Lameirinhas, R.A., João, J.P., Fernandes, C.A.F. (2021). Comparative study of the copper indium gallium selenide (CIGS) solar cell with other solar technologies. Sustainable Energy & Fuels, 5(8), 2273-2283. https://doi.org/10.1039/D0SE01717E
• 8. Castilho, C.D.S., Torres, J.P.N., Fernandes, C.A.F., Lameirinhas, R.A.M. (2021). Study on the Implementation of a Solar Photovoltaic System with Self-Consumption in an Educational Building. Energies, Vol. 14(8), 2214. https://doi.org/10.3390/EN14082214
• 9. Chomać-Pierzecka, E., Kokiel, A., Rogozińska-Mitrut, J., Sobczak, A., Soboń, D., Stasiak, J. (2022). Analysis and Evaluation of the Photovoltaic Market in Poland and the Baltic States. Energies, Vol. 15(2), 669. https://doi.org/10.3390/EN15020669
• 10. CLARIN-PL (n.d.). Retrieved from: http://clarin-pl.eu/, June 5, 2022
• 11. Corbett, J., Savarimuthu, B.T.R. (2022). From tweets to insights: A social media analysis of the emotion discourse of sustainable energy in the United States. Energy Research & Social Science, 89, 102515. https://doi.org/10.1016/J.ERSS.2022.102515
• 12. Das, S., Dutta, A., Medina, G., Minjares-Kyle, L., Elgart, Z. (2019). Extracting patterns from Twitter to promote biking. IATSS Research, 43(1), 51-59. https://doi.org/10.1016/j.iatssr.2018.09.002
• 13. Das, S., Sun, X., Dutta, A. (2015). Investigating User Ridership Sentiments for Bike Sharing Programs. Journal of Transportation Technologies, 5(2), 69-75. https://doi.org/10.4236/jtts.2015.52007
• 14. Decuypere, R., Robaeyst, B., Hudders, L., Baccarne, B., de Sompel, D. (2022). Transitioning to energy efficient housing: Drivers and barriers of intermediaries in heat pump technology. Energy Policy, 161, 112709.
• 15. Deng, H., Ergu, D., Liu, F., Cai, Y., Ma, B. (2022). Text sentiment analysis of fusion model based on attention mechanism. Procedia Computer Science, 199, 741-748.
• 16. Dincer, I. (2000). Renewable energy and sustainable development: a crucial review. Renewable and Sustainable Energy Reviews, 4(2), 157-175. https://econpapers.repec.org/RePEc:eee:rensus:v:4:y:2000:i:2:p:157-175
• 17. Eroğlu, H. (2021). Effects of Covid-19 outbreak on environment and renewable energy sector. Environment, Development and Sustainability, 23(4), 4782-4790. https://doi.org/10.1007/S10668-020-00837-4/FIGURES/5
• 18. Evans-Cowley, J.S., Griffin, G. (2012). Microparticipation with Social Media for Community Engagement in Transportation Planning. Transportation Research Record: Journal of the Transportation Research Board, 2307(1), 90-98. https://doi.org/10.3141/2307-10
• 19. Eyl-Mazzega, M.A., Mathieu, C. (2020). The European Union and the energy transition. Lecture Notes in Energy, 73, 27-46. https://doi.org/10.1007/978-3-030-39066-2_2/FIGURES/1
• 20. Grębosz-Krawczyk, M., Zakrzewska-Bielawska, A., Glinka, B., Glińska-Neweś, A. (2021). Why Do Consumers Choose Photovoltaic Panels? Identification of the Factors Influencing Consumers' Choice Behavior regarding Photovoltaic Panel Installations. Energies, Vol. 14(9), 2674. https://doi.org/10.3390/EN14092674
• 21. Grubljesic, T., Coelho, P.S., Jaklic, J. (2019). The Shift to Socio-Organizational Drivers of Business Intelligence and Analytics Acceptance. Journal of organizational and end user computing, 31(2), 37-64. https://doi.org/10.4018/JOEUC.2019040103
• 22. Hamilton, L.C., Hartter, J., Bell, E. (2019). Generation gaps in US public opinion on renewable energy and climate change. PLOS ONE, 14(7), e0217608. https://doi.org/10.1371/JOURNAL.PONE.0217608
• 23. Ibar-Alonso, R., Quiroga-García, R., Arenas-Parra, M. (2022). Opinion Mining of Green Energy Sentiment: A Russia-Ukraine Conflict Analysis. Mathematics, Vol. 10(14), 2532. https://doi.org/10.3390/MATH10142532
• 24. Jain, A., Jain, V. (2019a). Renewable Energy Sources for Clean Environment: Opinion Mining. Asian Journal of Water, Environment and Pollution, 16(2), 9-14. https://doi.org/10.3233/AJW190013
• 25. Jain, A., Jain, V. (2019b). Sentiment classification of twitter data belonging to renewable energy using machine learning. Https://Doi.Org/10.1080/02522667.2019.1582873, 40(2), 521-533. https://doi.org/10.1080/02522667.2019.1582873
• 26. Janz, A., Kocoń, J., Piasecki, M., Zaśko-Zielińska, M. (n.d.). plWordNet as a Basis for Large Emotive Lexicons of Polish CLARIN-PL View project Liner2-A Customizable Framework for Automatic Text Annotation (NER, TimeX, Events) View project. Retrieved from: https://www.researchgate.net/publication/322684200, June 5, 2022.
• 27. Kim, S.Y., Ganesan, K., Dickens, P., Panda, S. (2021). Public Sentiment toward Solar Energy-Opinion Mining of Twitter Using a Transformer-Based Language Model. Sustainability, Vol. 13(5), 2673. https://doi.org/10.3390/SU13052673
• 28. Lee, J. (2022). A Governance Structure Based on an Opinion Analysis of Local Stakeholders of Saemangeum Floating Photovoltaic Power Plants Project: Using Text Mining for Each Subject. Journal of People, Plants, and Environment, 25(6), 595-606. https://doi.org/10.11628/KSPPE.2022.25.6.595
• 29. Loureiro, M.L., 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
• 30. Moriarty, P., Honnery, D. (2011). What is the global potential for renewable energy? Renewable and Sustainable Energy Reviews, 16, 244-252. https://doi.org/10.1016/j.rser.2011.07.151
• 31. Mota, F., Neto Torres, J.P., Ferreira Fernandes, C.A., Marques Lameirinhas, R.A. (2020). Influence of an aluminium concentrator corrosion on the output characteristic of a photovoltaic system. Scientific Reports, 10(1), 1-16. https://doi.org/10.1038/s41598-020-78548-z
• 32. Muhammad, A.N., Bukhori, S., Pandunata, P. (2019). Sentiment Analysis of Positive and Negative of YouTube Comments Using Naïve Bayes - Support Vector Machine (NBSVM) Classifier. 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), 199-205. https://doi.org/10.1109/ICOMITEE.2019.8920923
• 33. Noblet, C.L., Teisl, M.F., Evans, K., Anderson, M.W., McCoy, S., Cervone, E. (2015). Public preferences for investments in renewable energy production and energy efficiency. Energy Policy, 87, 177-186. https://doi.org/10.1016/J.ENPOL.2015.09.003
• 34. Omnicore (2021). YouTube by the Numbers: Stats, Demographics & Fun Facts. Omnicoreagency.Com. https://www.omnicoreagency.com/youtube-statistics/
• 35. Omri, A., Daly, S., Nguyen, D.K. (2015). A robust analysis of the relationship between renewable energy consumption and its main drivers. Http://Dx.Doi.Org/10.1080/00036846.2015.1011312, 47(28), 2913-2923. https://doi.org/10.1080/00036846.2015.1011312
• 36. Pang, B., Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. ArXiv Preprint Cs/0409058.
• 37. Pang, B., Lee, L. (2008). Opinion mining and sentiment analysis. Found Trends Inf Retr, 2(1-2), 1-135.
• 38. Pang, B., Lee, L., Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques. Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing ({EMNLP} 2002), 79-86. https://doi.org/10.3115/1118693.1118704
• 39. Pellerin-Carlin, T., Vinois, J.-A., Rubio, E., Fernandes, S., Delors, J., Letta, E. (n.d.). Making the energy transition a european success tackling the democratic, innovation, financing and social challenges of the energy union.
• 40. Peñaloza, D., Mata, É., Fransson, N., Fridén, H., Samperio, Á., Quijano, A., Cuneo, A. (2022). Social and market acceptance of photovoltaic panels and heat pumps in Europe: A literature review and survey. Renewable and Sustainable Energy Reviews, 155, 111867. https://doi.org/10.1016/J.RSER.2021.111867
• 41. Peng, J., Lu, L., Yang, H. (2013). Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems. Renewable and Sustainable Energy Reviews, 19, 255-274. https://doi.org/10.1016/J.RSER.2012.11.035
• 42. Pestana, D.G., Rodrigues, S., Morgado-Dias, F. (2018). Environmental and economic analysis of solar systems in Madeira, Portugal. Utilities Policy, 55, 31-40. https://doi.org/10.1016/J.JUP.2018.09.001
• 43. Plutchik, R. (1980). Emotion: A Psychoevolutionary Synthesis. The American Journal of Psychology, 93(4), 751. https://doi.org/10.2307/1422394
• 44. Puzynina, J. (1992). Język wartości. 264.
• 45. Qazi, A., Hussain, F., Rahim, N.A.B.D., Hardaker, G., Alghazzawi, D., Shaban, K., Haruna, K. (2019). Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions. IEEE Access, 7, 63837-63851. https://doi.org/10.1109/ACCESS.2019.2906402
• 46. Quitzow, R., Bersalli, G., Eicke, L., Jahn, J., Lilliestam, J., Lira, F., Marian, A., Süsser, D., Thapar, S., Weko, S., Williams, S., Xue, B. (2021). The COVID-19 crisis deepens the gulf between leaders and laggards in the global energy transition. Energy Research & Social Science, 74, 101981. https://doi.org/10.1016/J.ERSS.2021.101981
• 47. Read, J. (2005). Using emoticons to reduce dependency in machine learning techniques for sentiment classification. Proceedings of the ACL Student Research Workshop, 43-48.
• 48. Salim, R.A., Rafiq, S. (2012). Why do some emerging economies proactively accelerate the adoption of renewable energy? Energy Economics, 34(4), 1051-1057. https://doi.org/10.1016/j.eneco.2011.08.015
• 49. Snelson, C. (2011). YouTube across the Disciplines : A Review of the Literature. Journal of Online Learning and Teaching, 7(1), 159-169. http://www.watchknow.org.
• 50. Stokes, L.C., Warshaw, C. (2017). Renewable energy policy design and framing influence public support in the United States. Nature Energy 2017 2:8, 2(8), 1-6. https://doi.org/10.1038/nenergy.2017.107
• 51. Twersky, C. (n.d.). scrapetube. Retrieved from: https://pypi.org/project/scrapetube/, June 13, 2023.
• 52. Wierzbicka, A. (1992a). Semantics, culture, and cognition : universal human concepts in culture-specific configurations. 487.
• 53. Wierzbicka, A. (1992b). Defining Emotion Concepts. Cognitive Science, 16(4), 539-581. https://doi.org/10.1207/S15516709COG1604_4
• 54. Xu, Q.A., Chang, V., Jayne, C. (2022). A systematic review of social media-based sentiment analysis: Emerging trends and challenges. Decision Analytics Journal, 3, 100073. https://doi.org/10.1016/J.DAJOUR.2022.100073
• 55. youtube-comment-downloader (n.d.). Retrieved from: https://pypi.org/project/youtube- comment-downloader/, June 13, 2023.
• 56. Zarrabeitia-Bilbao, E., Morales-I-gras, J., Rio-Belver, R.M., Garechana-Anacabe, G. (2022). Green energy: identifying development trends in society using Twitter data mining to make strategic decisions. Profesional de La Información, 31(1). https://doi.org/10.3145/EPI.2022.ENE.14
• 57. Zator, S., Lambert-Torres, G. (2021). Power Scheduling Scheme for DSM in Smart Homes with Photovoltaic and Energy Storage. Energies, Vol. 14(24), 8571. https://doi.org/10.3390/EN14248571
• 58. Zrównoważonego, M.I.-K., Energetycznego, R., Energetyki, W., Paliw, A., Mirowski, T., Sornek, K. (2015). Potential of prosumer power engineering in Poland by exampleof micro PV installation in private construction. Polityka Energetyczna - Energy Policy Journal, 18(2), 73-84. https://epj.min-pan.krakow.pl/Potential-of-prosumer-power-engineering-in-Poland-by-example-nof-micro-PV-installation,96084,0,2.html

Identyfikatory

DOI

10.29119/1641-3466.2023.185.36