Abstract
This article presents a sentiment analysis using data from X social media platform (Twitter) using artificial intelligence techniques. Two artificial intelligence techniques perform sentiment analysis: i) bag of words and ii) computer vision. The first is used for Natural Language Processing (NLP) and sentiment identification, while the second is for computer-based emotion identification in photographs or frames. The proposed methodology is applied to the soccer match between the Querétaro White Roosters and the Atlas Football Club in Guadalajara, Mexico. The study case involves 2,000 tweets from the March 5, 2022, soccer match, collected from Twitter, and 200 photographs/images taken on the game day. The experimental analysis examined data by NLP in R language and computer vision using DeepFace. Results indicate negative sentiment perceptions with similar percentages of 74% for NLP and 81% for DeepFace, with an average negative perception of 77.5%.
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References
Deepface. a lightweight face recognition and facial attribute analysis framework (age, gender, emotion, race) for python. https://pypi.org/project/deepface/ Accessed 27 Sept 27 2023
Instituto nacional de estadística y geografía. inegi, estado de ánimo de los tuiteros en méxico. (2020). https://www.inegi.org.mx/app/animotuitero/#/app/multiline Accessed 27 Sept 2023
Aïmeur, E., Amri, S., Brassard, G.: Fake news, disinformation and misinformation in social media: a review. Soc. Netw. Anal. Min. 13(1), 30 (2023)
Alsaeedi, A., Khan, M.Z.: A study on sentiment analysis techniques of twitter data. Int. J. Adv. Comput. Sci. Appl. 10(2), 361–374 (2019)
Alvarado, A.: Querétarovs.atlastragedia:¿quépasóenelpartidodelaligamxqueter- minó con muertos y suspendido? https://elpopular.pe/deportes/2022/03/06/mexico-queretaro-vs-atlas-tragedia-paso-partido-liga-mx-termino-muertos-suspendido-futbol-mexicano-video-114847 (2022) Accessed 28 Sept 2023
Anshari, M., Almunawar, M.N., Lim, S.A., Al-Mudimigh, A.: Customer relationship management and big data enabled: Personalization and customization of services. Appl. Comput. Inform. 15(2), 94–101 (2019)
Babu, N.V., Kanaga, E.G.M.: Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput. Sci. 3, 1–20 (2022)
documentation, R.: rtweet. https://www.rdocumentation.org/packages/rtweet/versions/1.1.0 (2023) Accessed 28 Sept 2023
Douglas, R.: 13 most popular sports in the world for 2023. https://theathleticbuild.com/most-popular-sports/ (2023) Accessed 11 Oct 2023
G. Martín, A., Fernández-Isabel, A., Martín de Diego, I., Beltrán, M.: A survey for user behavior analysis based on machine learning techniques: current models and applications. Applied Intell. 51(8), 6029–6055 (2021)
Habil, S., El-Deeb, S., El-Bassiouny, N.: AI-based recommendation systems: the ultimate solution for market prediction and targeting. In: The Palgrave Handbook of Interactive Marketing, pp. 683–704. Springer (2023). https://doi.org/10.1007/978-3-031-14961-0_30
Hung, M., et al.: Social network analysis of Covid-19 sentiments: Application of artificial intelligence. J. Med. Internet Res. 22(8), e22590 (2020)
Josimovski, S., Dodevski, D., et al.: Advantages of implementing artificial intelligence in e-business for consumers (2023)
Kumar, A., Garg, G.: Sentiment analysis of multimodal twitter data. Multimed. Tools Appl. 78, 24103–24119 (2019)
Lu, Y.: Artificial intelligence: a survey on evolution, models, applications and future trends. J. Manage. Anal. 6(1), 1–29 (2019)
Mancera, D.: 26 heridos, tres de gravedad y ningún detenido horas después de la batalla campal de querétaro. el paÍs. https://elpais.com/mexico/2022-03-06/26-heridos-3-de-gravedad-y-ningun-detenido-horas-despues-de-la-batalla-campal-de-queretaro.html (2022). Accessed 28 Sept 2023
Mendoza-Vega, J.: Análisis de sentimientos con r - léxico afinn. https://rpubs.com/jboscomendoza/analisis_sentimientos_lexico_afinn (2018). Accessed Sept 27 2023
Mendoza-Vega, J.: Léxico afinn. https://raw.githubusercontent.com/jboscomendoza/rpubs/master/sentimientos_afinn/lexico_afinn.en.es.csv (2018) Accessed 27 Sept 2023
Murillo, M.: Gobernadordequerétaro, mauriciokuri, confirma26heridosyquenohubo muertos, tras lo ocurrido en el querétaro vs atlas. uno tv. https://www.unotv.com/deportes/gobierno-de-queretaro-confirma-26-heridos-y-que-no-hubo-muertos-tras-lo-ocurrido-en-el-queretaro-vs-atlas/ (2022) Accessed 28 Sept 2023
Najera-Salmeron, J.: Fundamentos del análisis de redes sociales en r: Introducción teórica y práctica al análisis de redes sociales para mercadólogos e investigadores sociales, spanish Tech. rep, México (2022)
Navarro, J.: Atlas: 26 heridos \(|\) resumen y últimas noticias, as méxico-diario as. https://mexico.as.com/mexico/2022/03/06/futbol/1646527770_037496.html (2022) Accessed 28 Sept 2023
Peng, S., Wang, G., Xie, D.: Social influence analysis in social networking big data: opportunities and challenges. IEEE Netw. 31(1), 11–17 (2017). https://doi.org/10.1109/MNET.2016.1500104NM
Rustam, F., Ashraf, I., Mehmood, A., Ullah, S., Choi, G.S.: Tweets classification on the base of sentiments for us airline companies. Entropy 21(11) (2019). https://doi.org/10.3390/e21111078
Serengil, S.I., Ozpinar, A.: Lightface: A hybrid deep face recognition framework. In: 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–5 (2020)
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., Liu, Y.: Combating fake news: a survey on identification and mitigation techniques. ACM Trans. Intell. Syst. Technol. (TIST) 10(3), 1–42 (2019)
SNStaff: Querétaro-atlas, incidentes, horrorysuspensión:lasúltimasnovedadesdelhecho que conmueve a méxico. the sporting news. https://www.sportingnews.com/mx/liga-mx/news/invasion-cancha-atlas-queretaro-liga-mx-suspension/pdvc7cfahc5ywjkwqiiqqscp (2022) Accessed 28 Sept 2023
SportyTell, E.: Top-10 most popular sports in the world 2023. https://sportytell.com/sports/most-popular-sports-world/ (2023) Accessed 23 Nov 2023
Wang, C., Xiao, Z., Liu, Y., Xu, Y., Zhou, A., Zhang, K.: Sentiview: sentiment analysis and visualization for internet popular topics. IEEE Trans. Human-Mach. Syst. 43(6), 620–630 (2013)
Wong, Q., Skillings, J.: Twitter’susergrowthsoarsamidcoronavirus, butuncertaintyremains. cnet. 2020 apr 30. https://www.cnet.com/tech/mobile/twitters-user-growth-soars-amid-coronavirus-but-uncertainty-remains/ (2022) Accessed 28 Sept 2023
Zhao, K., Xu, J., Cheng, M.M.: Regularface: deep face recognition via exclusive regularization. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1136–1144 (2019). https://doi.org/10.1109/CVPR.2019.00123
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Hernández-Aguilar, J.A. et al. (2024). A New Sentiment Analysis Methodology for Football Game Matches Utilizing Social Networks and Artificial Intelligence Techniques. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-Cities 2023. Communications in Computer and Information Science, vol 1938. Springer, Cham. https://doi.org/10.1007/978-3-031-52517-9_15
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