The aim of this study was to share our experience of developing a digital Natural Language Processing Tool and its implementation in the process of training future linguists. In this article, we demonstrate the process of creating the web application SENTIALIZER, which is a multilingual Sentiment Analysis Tool developed with the help of the Python programming language and its libraries NLTK, BS4, TextBlob, Googletrans. The integration of Sentiment Analysis Tools into the educational framework is relied on the Unified Theory of Acceptance and Use of Technology (UTAUT) as its foundation. The results show that students see the prospects of using Sentiment Analysis Tools in their educational and professional activities, are ready to use them in the future, but are not ready to participate personally in projects to develop and improve such technologies. The reasons for this attitude are discussed. The presented study has a clear focus on student learning outcomes, which is an important criterion for the successful integration of technology into the educational process.
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Basmmi, A. B., Halim, S. A., & Saadon, N. A. (2020). Comparison of web services for sentiment analysis in social networking sites. Proceedings of the IOP conference series: Materials science and engineering, Malaysia, 884, 012063. https://dx.doi.org/10.1088/1757-899X/884/1/012063
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Bouznif, M. (2018). Business students’ continuance intention toward blackboard usage: an empirical investigation of UTAUT model. International Journal of Business and Management, 13(1), 120-130. https://doi.org/10.5539/ijbm.v13n1p120
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Bueno, I., Carrasco, R., Ureña, R., & Herrera-Viedma, E. (2022). A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Information Sciences, 589, 300-320. https://doi.org/10.1016/j.ins.2021.12.080
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Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN computer science, 3(74). https://doi.org/10.1007/s42979-021-00958-1
Banea, C., Mihalcea, R., Wiebe, J., & Hassan, S. (2008). Multilingual subjectivity analysis using machine translation. In M. Lapata, & H. T. Ng (Eds), Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08) (pp. 127-135). Association for Computational Linguistics. http://dx.doi.org/10.3115/1613715.1613734
Barab S. A., Squire K. D., & Dueber W. (2000). A co-evolutionary model for supporting the emergence of authenticity. Educational Technology Research and Development, 48(2), 37-62. https://doi.org/10.1007/BF02313400
Baskara, R., & Mukarto, M. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 7(2), 343-358.
Basmmi, A. B., Halim, S. A., & Saadon, N. A. (2020). Comparison of web services for sentiment analysis in social networking sites. Proceedings of the IOP conference series: Materials science and engineering, Malaysia, 884, 012063. https://dx.doi.org/10.1088/1757-899X/884/1/012063
Ben Youssef, A., Dahmani, M., & Omrani, N. (2015). Information technologies, students’ e-skills and diversity of learning process. Education and Information Technologies, 20, 141-159. https://doi.org/10.1007/s10639-013-9272-x
Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134
Bisio, F., Oneto, L., & Cambria, E. (2017). Sentic computing for social network analysis. In F.A. Pozzi, E. Fersini, E. Messina, & B. Liu (Eds.), Sentiment Analysis in Social Networks (pp. 71-99). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-804412-4.00005-X
Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter O. (2018). Digital transformation in German higher education: student and teacher perceptions and usage of digital media. International Journal of Educational Technology in Higher Education, 15, 48. https://doi.org/10.1186/s41239-018-0130-1
Boukes, M., van de Velde, B., Araujo, T., & Vliegenthart, R. (2019). What’s the tone? Easy doesn’t do it: Analyzing performance and agreement between off-the-shelf sentiment analysis tools. Communication Methods and Measures, 14(2), 83-104. https://doi.org/10.1080/19312458.2019.1671966
Bouznif, M. (2018). Business students’ continuance intention toward blackboard usage: an empirical investigation of UTAUT model. International Journal of Business and Management, 13(1), 120-130. https://doi.org/10.5539/ijbm.v13n1p120
Boyd, R. L., & Schwartz, H. A. (2021). Natural language analysis and the psychology of verbal behavior: The past, present, and future states of the field. Journal of Language and Social Psychology, 40(1), 21-41. https://doi.org/10.1177/0261927X20967028
Bueno, I., Carrasco, R., Ureña, R., & Herrera-Viedma, E. (2022). A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Information Sciences, 589, 300-320. https://doi.org/10.1016/j.ins.2021.12.080
Chapple, D. G., Weir, B., & Martin, R. S. (2017). Can the incorporation of quick response codes and smartphones improve field-based science education? International Journal of Innovation in Science and Mathematics Education, 25(2), 49-71.
Chauhan, P., Sharma, N., & Sikka, G. (2021). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12, 2601-2627. https://doi.org/10.1007/s12652-020-02423-y
Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer F. (2020). Simulation-based learning in higher education: a meta-analysis. Review of Educational Research, 90(4), 499-541. https://doi.org/10.3102/0034654320933544
Contreras, D., Wilkinson, S., Alterman, E., & Hervás, J. (2022). Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake. Nat Hazards, 113, 403-421 https://doi.org/10.1007/s11069-022-05307-w
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