Sentiment Analysis Concerning Heat Pumps - Analysis of Tweets Published in Polish

• Autorzy: Marcin Wyskwarski
• Języki publikacji: EN

Abstrakty

EN

Purpose: Identifying thoughts, feelings and opinions on "heat pumps" based on the content of tweets. Design/methodology/approach: Tweets written in Polish, containing all possible grammatical cases of the terms "heat pump" and "heat pumps", were automatically downloaded. The content of the tweets has been preprocessed. URLs, hashtags, emojis, usernames, all characters except letters, and phrases used to search for tweets were removed from their content. The sentiment value of the tweets was calculated. Visualisations were prepared to show the percentage of positive, negative and neutral tweets. The most frequently used words in tweets were shown with word clouds. Findings: The number of tweets concerning heat pumps and the percentage of positive, negative and neutral tweets were determined. Research limitations/implications: Only the content of tweets written in Polish was analysed. Sentiment analysis was performed automatically by the service "ccl_emo", without author supervision. Only the opinions of people who posted on Twitter were analysed. Practical implications: Automatic monitoring of people's feelings about heat pumps. Originality/value: Information on the attitudes of people from Poland towards heat pumps was obtained. It has been established, based on a growing number of tweets, that interest in heat pumps in Poland is growing all the time.(original abstract)

Słowa kluczowe

EN

Sentiment analysis; Social media; Heat pumps

PL

Analiza wydźwięku; Media społecznościowe; Pompy ciepła

• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2022
• Numer: z. 167 Contemporary Challenges in the Performance of Organisations = Współczesne wyzwania organizacji
• Strony: 609--621

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. Akshay Java Xiaodan Song, T.F., Tseng, B. (2007). Why We Twitter: Understanding Microblogging Usage and Communities. Procedings of the Joint 9th WEBKDD and 1st SNA-KDD Workshop 2007, Springer, 56-65.
• 3. Alqaraleh, S. (2020). Turkish Sentiment Analysis System via Ensemble Learning. Avrupa Bilim ve Teknoloji Dergisi, 122-129.
• 4. Antypas, D., Preece, A., Collados, J.C. (2022). Politics and Virality in the Time of Twitter: A Large-Scale Cross-Party Sentiment Analysis in Greece, Spain and United Kingdom. Retrieved from: http://arxiv.org/abs/2202.00396.
• 5. Ayan, B., Kuyumcu, B., Ciylan, B. (2019). Detection of Islamophobic Tweets on Twitter Using Sentiment Analysis. Gazi University Journal of Science Part C, 7(2), 495-502.
• 6. Bergman, N. (2013). Why is renewable heat in the UK underperforming? A socio-technical perspective. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 227(1), 124-131.
• 7. Billimoria, S., Guccione, L., Henchen, M., Louis-Prescott, L. (2021). The Economics of Electrifying Buildings: How Electric Space and Water Heating Supports Decarbonization of Residential Buildings. World Scientific Encyclopedia of Climate Change: Case Studies of Climate Risk, Action, and Opportunity, Vol. 3 (pp. 297-304). World Scientific.
• 8. Blair, J., Hsu, C.Y., Qiu, L., Huang, S.H., Huang, T.H.K., Abdullah, S. (2021). Using Tweets to Assess Mental Well-being of Essential Workers during the COVID-19 Pandemic. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411763.3451612
• 9. Boyd, D., Golder, S., Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. Proceedings of the Annual Hawaii International Conference on System Sciences, 1-10. https://doi.org/10.1109/HICSS.2010.412
• 10. Bruns, A. (2012). How long is a tweet? Mapping dynamic conversation networks on Twitter using Gawk and Gephi. Information Communication and Society, 15(9), 1323-1351. https://doi.org/10.1080/1369118X.2011.635214.
• 11. Chinnasamy, P., Suresh, V., Ramprathap, K., Jebamani, B.J.A., Rao, K.S., Kranthi, M.S. (2022). COVID-19 vaccine sentiment analysis using public opinions on Twitter. Materials Today: Proceedings.
• 12. Chua, K.J., Chou, S.K., Yang, W.M. (2010). Advances in heat pump systems: A review. Applied Energy, 87(12), 3611-3624.
• 13. CLARIN-PL (n.d.). Retrieved from: http://clarin-pl.eu/, June 5, 2022.
• 14. Çoban, Ö., Tümüklü Özyer, G. (2018). The impact of term weighting method on Twitter sentiment analysis. Pamukkale University Journal Of Engineering Sciences-Pamukkale Universitesi Muhendislik Bilimleri Dergisi, 24(2).
• 15. 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.
• 16. 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.
• 17. 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.
• 18. 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.
• 19. Edenhofer, O. (2015). Climate change 2014: mitigation of climate change (Vol. 3). Cambridge University Press.
• 20. 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.
• 21. Fadel, I., Cemil, Ö.Z. (2020). A sentiment analysis model for terrorist attacks reviews on Twitter. Sakarya University Journal of Science, 24(6), 1294-1302.
• 22. Gabarron, E., Dechsling, A., Skafle, I., Nordahl-Hansen, A. et al. (2022). Discussions of Asperger Syndrome on Social Media: Content and Sentiment Analysis on Twitter. JMIR Formative Research, 6(3), e32752.
• 23. Garcia, K., Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057. https://doi.org/10.1016/j.asoc.2020.107057.
• 24. Go, A., Huang, L., Bhayani, R. (2009). Twitter sentiment analysis. Entropy, 17, 252.
• 25. 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.
• 26. Honeycutt, C., Herring, S.C. (2009). Beyond microblogging: Conversation and collaboration via twitter. Proceedings of the 42nd Annual Hawaii International Conference on System Sciences, HICSS, 1-10. https://doi.org/10.1109/HICSS.2009.89.
• 27. 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.
• 28. Kircher, K.J., Zhang, K.M. (2021). Heat purchase agreements could lower barriers to heat pump adoption. Applied Energy, 286, 116489.
• 29. Martiskainen, M., Schot, J., Sovacool, B.K. (2021). User innovation, niche construction and regime destabilization in heat pump transitions. Environmental Innovation and Societal Transitions, 39, 119-140.
• 30. Nezhad, Z.B., Deihimi, M.A. (2022). Twitter sentiment analysis from Iran about COVID-19 vaccine. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 16(1), 102367.
• 31. Nkonde, M., Rodriguez, M.Y., Cortana, L., Mukogosi, J.K., King, S., Serrato, R., ... Malik, M.M. (2021). Retracted: Disinformation creep: ADOS and the strategic weaponization of breaking news. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/ MR-2020-52.
• 32. Nowak, T. (2018). Heat Pumps: Integrating technologies to decarbonise heating and cooling. European Copper Institute.
• 33. Panagiotopoulos, P., Sams, S. (2012). An overview study of Twitter in the UK local government, 1-13.
• 34. Pang, B., Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. ArXiv Preprint Cs/0409058.
• 35. Pang, B., Lee, L. (2008). Opinion mining and sentiment analysis. Found. Trends. Inf. Retr., 2(1-2), 1-135.
• 36. Read, J. (2005). Using emoticons to reduce dependency in machine learning techniques for sentiment classification. Proceedings of the ACL Student Research Workshop, 43-48.
• 37. Sarkar, T., Rajadhyaksha, N. (2021). TLA: Twitter Linguistic Analysis. Retrieved from: https://arxiv.org/abs/2107.09710v1.
• 38. Sarlan, A., Nadam, C., Basri, S. (2014). Twitter sentiment analysis. Proceedings of the 6th International Conference on Information Technology and Multimedia, 212-216.
• 39. Small, T.A. (2011). What the hashtag?: A content analysis of Canadian politics on Twitter. Information Communication and Society, 14(6), 872-895. https://doi.org/10.1080/ 1369118X.2011.554572.
• 40. Snape, J.R., Boait, P.J., Rylatt, R.M. (2015). Will domestic consumers take up the renewable heat incentive? An analysis of the barriers to heat pump adoption using agent-based modelling. Energy Policy, 85, 32-38.
• 41. Soltani, M., Kashkooli, F.M., Dehghani-Sanij, A.R., Kazemi, A.R., Bordbar, N., Farshchi, M.J., ... Dusseault, M.B. (2019). A comprehensive study of geothermal heating and cooling systems. Sustainable Cities and Society, 44, 793-818.
• 42. Sunitha, D., Patra, R.K., Babu, N.V, Suresh, A., Gupta, S.C. (2022). Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries. Pattern Recognition Letters, 158, 164-170.
• 43. Ürge-Vorsatz, D., Cabeza, L.F., Serrano, S., Barreneche, C., Petrichenko, K. (2015). Heating and cooling energy trends and drivers in buildings. Renewable and Sustainable Energy Reviews, 41, 85-98.
• 44. 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/https://doi.org/10.1016/j.dajour.2022.100073.
• 45. Zavattaro, S.M., French, P.E., Mohanty, S.D. (2015). A sentiment analysis of U.S. local government tweets: The connection between tone and citizen involvement. Government Information Quarterly, 32, 333-341. https://doi.org/10.1016/j.giq.2015.03.003

Identyfikatory

DOI

10.29119/1641-3466.2022.167.42