Sentiment analysis of online user comments on artificial meat

Yıl 2024, Sayı: Special Issue 2 - Sustainability, Innovation and Changing Dynamics in Tourism: From Local to Global, 33 - 43

Merve Onur

Öz

Artificial meat is a sustainable protein source that has riveted attention recently. However, differences of opinion have led to the need for more research on the issue. The controversy complicates the assessment of whether or not artificial meat will potentially be consumed in the future. This study aimed to determine the emotional states of YouTube users toward artificial meat. For this purpose, YouTube was used as a considerable data source in determining individuals' emotions and opinions. User comments on popular videos about “artificial meat” shared on online were evaluated using sentiment analysis (SA). They were classified as positive, neutral, and negative according to their polarity scores in the lexicon-based SA method. Analysis results demonstrated that 11,113 (40.8%) of the user comments were positive, 9,054 (33.2%) were negative, and 7,064 (25.9%) were neutral. The most frequently repeated words were “meat, eat, and like,” while the most frequent negative words were “fake, cancer, synthetic and expensive” respectively.

Anahtar Kelimeler

Artificial Meat, User Opinions, Sentiment Analysis, Natural Language Processing, Online Comments

Kaynakça

• Alhujaili, R. F., & Yafooz, W. M. (2021, March). Sentiment analysis for YouTube videos with user comments. In 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS) (pp. 814–820). IEEE. https://doi.org/10.1109/ICAIS50930.2021.9396049
• Amarasekara, I., & Grant, W. J. (2019). Exploring the YouTube science communication gender gap: A sentiment analysis. Public Understanding of Science, 28(1), 68–84. https://doi.org/10.1177/0963662518786654
• Arazy, O., & Woo, C. (2007). Enhancing information retrieval through statistical natural language processing: A study of collocation indexing. MIS Quarterly, 31(3), 525–546. https://doi.org/10.2307/25148806
• Asioli, D., Bazzani, C., & Nayga, Jr, R. M. (2022). Are consumers willing to pay for in-vitro meat? An investigation of naming effects. Journal of Agricultural Economics, 73(2), 356–375. https://doi.org/10.1111/1477-9552.12467
• Aslan, B., & Erdur, R. C. (2020, October). Stock market prediction with deep learning using public disclosure platform data. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1–5). IEEE. https://doi.org/10.1109/ASYU50717.2020.9259836
• Aslan, S. (2023). Aspect-based sentiment analysis in e-commerce user reviews using natural language processing techniques. Fırat University Journal of Engineering Science, 35(2), 875–882. https://doi.org/10.35234/fumbd.1335583
• Baran, A. (2020). In vitro ete karşı olan tutumun araştırılması: Erzurum Meslek Yüksekokulu öğrencileri örneği. Harran Üniversitesi Veteriner Fakültesi Dergisi, 9(2), 98–106.
• Bhat, Z. F., Kumar, S., & Fayaz, H. (2015). In vitro meat production: Challenges and benefits over conventional meat production. Journal of Integrative Agriculture, 14(2), 241–248. https://doi.org/10.1016/S2095-3119(14)60887-X
• Bhuiyan, H., Ara, J., Bardhan, R., & Islam, M. R. (2017, September). Retrieving YouTube videos by sentiment analysis on user comments. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 474–478). IEEE. https://doi.org/10.1109/ICSIPA.2017.8120658
• Bonny, S. P. F., Gardner, G. E., Pethick, D. W., & Hocquette, J.-F. (2015). What is artificial meat and what does it mean for the future of the meat industry? Journal of Integrative Agriculture, 14(2), 255–263. https://doi.org/10.1016/S2095-3119(14)60888-1
• Boudad, N., Faizi, R., Rachid, O. H., & Chiheb, R. (2017). Sentiment analysis in Arabic: A review of the literature. Ain Shams Engineering Journal, 9(4), 2479–2490. https://doi.org/10.1016/j.asej.2017.04.007
• Brossoie, N., Roberto, K. A., & Barrow, K. M. (2012). Making sense of intimate partner violence in late life: Comments from online news readers. Gerontologist, 52, 792–801. https://doi.org/10.1093/geront/gns046
• Bryant, C., & Barnett, J. (2020). Consumer acceptance of cultured meat: An updated review (2018–2020). Applied Sciences, 10(15), Article 5201. https://doi.org/10.3390/app10155201
• Choudhury, D., Tseng, T. W., & Swartz, E. (2020). The business of cultured meat. Trends in Biotechnology, 38(6), 573–577.
• Da Silva, B. D., & Conte-Junior, C. A. (2024). Perspectives on cultured meat in countries with economies dependent on animal production: A review of potential challenges and opportunities. Trends in Food Science & Technology. https://doi.org/10.1016/j.tifs.2023.104551
• De Souza-Vilela, J., Andrew, N. R., & Ruhnke, I. (2019). Insect protein in animal nutrition. Animal Production Science, 59(11), 2029–2036. https://doi.org/10.1071/AN19255
• Demir, Y. E., Durmaz, S., Elbir, A., Sigirci, I. O., & Diri, B. (2020, October). Sentiment analysis for hotel attributes from online reviews. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) (pp. 1–4). IEEE. https://doi.org/10.1109/ASYU50717.2020.9259823
• Dierbach, C. (2012). Introduction to computer science using Python: A computational problem-solving focus. Wiley Publishing. https://doi.org/10.5555/2835377.2835396
• Dupont, J., & Fiebelkorn, F. (2020). Attitudes and acceptance of young people toward the consumption of insects and cultured meat in Germany. Food Quality and Preference, 103, Article 103983. https://doi.org/10.1016/j.foodqual.2020.103983
• Farhoomand, D., Okay, A., Aras, S., & Büyük, İ. (2022). Yapay et üretimi ve gelecek vizyonu. Food and Health, 8(3), 260–272.
• Gilbert, N. (2010). How to feed a hungry world: Producing enough food for the world’s population in 2050 will be easy, but doing it at an acceptable cost to the planet will depend on research into everything from high-tech seeds to low-tech farming practices. Nature, 466, 531–532. https://doi.org/10.1038/466531a
• Good Meat. (2023). GOOD meat gets full approval in the U.S. for cultivated meat. GOOD Meat. https://www.goodmeat.co/all-news/good-meat-gets-full-approval-in-the-us-for-cultivated-meat
• Goodwin, J. N., & Shoulders, C. W. (2013). The future of meat: A qualitative analysis of cultured meat media coverage. Meat Science, 95, 445–450. https://doi.org/10.1016/j.meatsci.2013.05.027
• Haagsman, H. P., Hellingwerf, K. J., & Roelen, B. A. J. (2009). Production of animal proteins by cell systems. Faculty of Veterinary Medicine, University of Utrecht. Retrieved from https://psu.edu
• Heimerl, F., Lohmann, S., Lange, S., & Ertl, T. (2014). Word cloud explorer: Text analytics based on word clouds. In 2014 47th Hawaii International Conference on System Sciences (HICSS) (pp. 1833–1842). IEEE. https://doi.org/10.1109/HICSS.2014.231
• Hoek, A. C., Van Boekel, M. A., Voordouw, J., & Luning, P. A. (2011). Identification of new food alternatives: How do consumers categorize meat and meat substitutes? Food Quality and Preference, 22(4), 371–383.
• Hocquette, J. F. (2016). Is in vitro meat the solution for the future? Meat Science, 12, 167–176. https://doi.org/10.1016/j.meatsci.2016.04.036
• Hutto, C., & Gilbert, E. (2014). VADER: A parsimonious rule-based model for sentiment analysis of social media text. Eighth International AAAI Conference on Weblogs and Social Media, 8(1), 216–225. https://doi.org/10.1609/icwsm.v8i1.14550
• Jagtap, V. S., & Pawar, K. (2013). Analysis of different approaches to sentence-level sentiment classification. International Journal of Scientific Engineering and Technology, 2(3), 164–170. Retrieved from https://d1wqtxts1xzle7.cloudfront.net
• Kushal, D., Steve, L., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of WWW’03, 12th International Conference on World Wide Web (pp. 519–528). https://doi.org/10.1145/775152.775226
• Laestadius, L. I., & Caldwell, M. A. (2015). Is the future of meat palatable? Perceptions of in vitro meat as evidenced by online news comments. Public Health Nutrition, 18(13), 2457–2467. https://doi.org/10.1017/S1368980015000622
• Liu, W., Hao, Z., Florkowski, W. J., Wu, L., & Yang, Z. (2022). A review of the challenges facing global commercialization of the artificial meat industry. Foods, 11(22), Article 3609. https://doi.org/10.3390/foods11223609
• Loke, J. (2013). Readers’ debate a local murder trial: ‘Race’ in the online public sphere. Communication, Culture & Critique, 6, 179–200. https://doi.org/10.1111/j.1753-9137.2012.01139.x
• Machová, K., & Marhefka, L. (2013). Opinion mining in conversational content within web discussions and commentaries. In International Conference on Availability, Reliability, and Security (pp. 149–161). Springer. Retrieved from https://link.springer.com
• Mancini, M. C., & Antonioli, F. (2019). Exploring consumers’ attitude towards cultured meat in Italy. Meat Science, 150, 101–110. https://doi.org/10.1016/j.meatsci.2018.12.014
• Mariasegaram, M., Harrison, B. E., Bolton, J. A., Tier, B., Henshall, J. M., Barendse, W., & Prayaga, K. C. (2012). Fine-mapping the POLL locus in Brahman cattle yields the diagnostic marker CSAFG29. Animal Genetics, 43(6), 683–688. https://doi.org/10.1111/j.1365-2052.2012.02336.x
• Mateti, T., Laha, A., & Shenoy, P. (2022). Artificial meat industry: Production methodology, challenges, and future. JOM, 74(9), 3428–3444. Retrieved from https://link.springer.com
• Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
• OECD-FAO. (2013). Agricultural outlook 2012–2021. Retrieved from https://reliefweb.int/report/world/oecd-fao-agricultural-outlook-2013
• Özyurt, B., & Akcayol, M. A. (2018). A survey on sentiment analysis and opinion mining methods and approaches. Selcuk University Journal of Engineering Science and Technology, 6(4), 668–693. https://doi.org/10.15317/Scitech.2018.160
• Pakseresht, A., Kaliji, S. A., & Canavari, M. (2022). Review of factors affecting consumer acceptance of cultured meat. Appetite, 170, Article 105829. https://doi.org/10.1016/j.appet.2021.105829
• Poria, Y., & Oppewal, H. (2003). A new medium for data collection: Online news discussions. International Journal of Contemporary Hospitality Management, 15(4), 232–236. https://doi.org/10.1108/09596110310475694
• Post, M. J. (2012). Cultured meat from stem cells: Challenges and prospects. Meat Science, 92(3), 297–301. https://doi.org/10.1016/j.meatsci.2012.04.008
• Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches, and applications. Knowledge-Based Systems, 89, 14–46. https://doi.org/10.1016/j.knosys.2015.06.015
• Shan, L., Jiao, X., Wu, L., Shao, Y., & Xu, L. (2022). Influence of framing effect on consumers’ purchase intention of artificial meat—Based on empirical analysis of consumers in seven cities. Frontiers in Psychology, 13, Article 911462. https://doi.org/10.3389/fpsyg.2022.911462
• Shen, Y. C., & Chen, H. S. (2020). Exploring consumers’ purchase intention of an innovation in the agri-food industry: A case of artificial meat. Foods, 9(6), 745. https://doi.org/10.3390/foods9060745
• Shirsat, V. S., Jagdale, R. S., & Deshmukh, S. N. (2017). Document-level sentiment analysis from news articles. In International Conference on Computing, Communication, Control and Automation (ICCUBEA) (pp. 1–4). IEEE. https://doi.org/10.1109/ICCUBEA.2017.8463638
• Siddiqui, S. A., Khan, S., Murid, M., Asif, Z., Oboturova, N. P., Nagdalian, A. A., Blinov, A. V., Ibrahim, S. A., & Jafari, S. M. (2022). Marketing strategies for cultured meat: A review. Applied Sciences, 12(17), Article 8795. https://doi.org/10.3390/app12178795
• Singh, R., & Tiwari, A. (2021). YouTube comments sentiment analysis. International Journal of Scientific Research in Engineering and Management, 5(5), 1–11. Retrieved from https://www.researchgate.net
• Slade, P. (2018). If you build it, will they eat it? Consumer preferences for plant-based and cultured meat burgers. Appetite, 125, 428–437. https://doi.org/10.1016/j.appet.2018.02.030
• Tetsuya, N., & Jeonghee, Y. (2003). Sentiment analysis: Capturing favorability using natural language processing. In Proceedings of KCAP-03, 2nd International Conference on Knowledge Capture (pp. 70–77). https://doi.org/10.1145/945645.945658
• Turney, P. D. (2002). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL’02, 40th Annual Meeting of the Association for Computational Linguistics (pp. 417–424). https://doi.org/10.48550/arXiv.cs/0212032
• UPSIDE Foods. (2023). UPSIDE is approved for sale in the U.S.! Here’s what you need to know. UPSIDE Foods. Retrieved from https://www.upsidefoods.com
• Ünver Alçay, A., Sağlam, A., Yalçın, S., & Bostan, K. (2018). Possible protein sources for the future. Akademik Gıda, 16(2), 197–204. https://doi.org/10.24323/akademik-gida.449865
• Varma, V., Kurisinkel, L. J., & Radhakrishnan, P. (2017). Social media summarization. In Cambria, E., Das, D., Bandyopadhyay, S., & Feraco, A. (Eds.), A practical guide to sentiment analysis (pp. 135–153). Springer. Retrieved from https://link.springer.com
• Verbeke, W., Sans, P., & Van Loo, E. J. (2015). Challenges and prospects for consumer acceptance of cultured meat. Journal of Integrative Agriculture, 14(2), 285–294.
• Welin, S. (2013). Introducing the new meat: Problems and prospects. Etikk i Praksis-Nordic Journal of Applied Ethics, 7(1), 24–37. https://doi.org/10.5324/eip.v7i1.1788
• Wilks, M., & Phillips, C. J. C. (2017). Attitudes to in vitro meat: A survey of potential consumers in the United States. PloS One, 12(2), Article e0171904. https://doi.org/10.1371/journal.pone.0171904
• Wilson, T., Wiebe, J., & Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (pp. 347–354). Retrieved from https://psu.edu
• Yaşa, H. (2022). Çevre(cilik) hareketi olarak sosyal medyada sıfır atık hareketi. Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 49, 212–230. https://doi.org/10.52642/susbed.1156189
• Yousef, A. H., Medhat, W., & Mohamed, H. K. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
• Zhang, L., Hu, Y., Badar, I. H., Xia, X., Kong, B., & Chen, Q. (2021). Prospects of artificial meat: Opportunities and challenges around consumer acceptance. Trends in Food Science & Technology, 116, 434–444. https://doi.org/10.1016/j.tifs.2021.07.010
• Zhang, M., Li, L., & Bai, J. (2020). Consumer acceptance of cultured meat in urban areas of three cities in China. Food Control, 118, Article 107390. https://doi.org/10.1016/j.foodcont.2020.107390.