Comparison of Sentiment Analysis in Internship Feedback from Hospitality, Educational Institutions, and Other Industries: An Analysis Using TextBlob and VADER

ผู้แต่ง

  • HIDEYUKI SUZUKI -
  • Budsabong Preedawongsakorn
  • Naowarat Assavatesamongkol
  • Rossukhon ็Hemara
  • Patcharaporn Rattanawaropas
  • Sopida Sereesuchat

บทคัดย่อ

          This quantitative research examines how VADER and TextBlob sentiment analysis tools process feedback data from the host organizations of 124 Thai university interns. The research uses supervisor evaluations from hospitality, education institutions, and other industries to determine how each tool identifies positive, negative, and neutral sentiments. The MannU test was applied to compare the sentiment scores produced from preprocessed internship comments by two analysis tools. The research showed that education sector feedback received similar assessments from both tools despite the apparent difference in polarity spread, while hospitality and other sectors produced distinct results despite showing similarity in the polarity terms. The research indicates that sentiment interpretation depends on the analysis tool, suggesting the need to understand the fundamental discrepancy between these tools in labelling negativity and positivity for feedback on internships.

เอกสารอ้างอิง

Aftab, F., Sibghat Ullah Bazai, Shah Marjan, Baloch, L., Aslam, S., Amphawan, A., & Tse Kian Neo. (2023). A Comprehensive Survey on Sentiment Analysis Techniques. International Journal of Technology: IJ Tech, 14(6), 1288–1288. https://doi.org/10.14716/ijtech.v14i6.6632

Agarwal, B., & Mittal, N. (2015). Machine Learning Approach for Sentiment Analysis. Socio-Affective Computing, 21–45. https://doi.org/10.1007/978-3-319-25343-5_3

Aguilar-Moreno, J. A., Palos-Sanchez, P. R., & R. Pozo-Barajas. (2024). Sentiment analysis to support business decision-making. A bibliometric study. AIMS Mathematics, 9(2), 4337–4375. https://doi.org/10.3934/math.2024215

Ansari, M.Z., Aziz, M.B., Siddiqui, M.O., Mehra, H., & Singh, K.P. (2020). Analysis of Political Sentiment Orientations on Twitter. Procedia Computer Science, 167, 1821–1828. https://doi.org/10.1016/j.procs.2020.03.201

Araci, D. (2019). FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.1908.10063

Bose, R., Dey, R.K., Roy, S., Sarddar, D. (2020). Sentiment analysis on online product reviews, Information and Communication Technology for Sustainable Development: Proceedings of ICT4SD 2018. Springer, pp. 559-569.

Chaturvedi, I., Cambria, E., Welsch, R. E., & Herrera, F. (2018). Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Information Fusion, 44(44), 65–77. https://doi.org/10.1016/j.inffus.2017.12.006

Dahal, K.R., Gupta, A., & Budhathoki, N. (2025). Comparative Analysis of VADER and TextBlob on Financial News Headlines. Journal of Data Science: JDS, 1–20. https://doi.org/10.6339/25-JDS1195

Denecke, K., & Reichenpfader, D. (2023). Sentiment analysis of clinical narratives: A scoping review. Journal of Biomedical Informatics, 140, 104336. https://doi.org/10.1016/j.jbi.2023.104336

Deng, J., & Liu, Y. (2025). Research on Sentiment Analysis of Online Public Opinion Based on RoBERTa–BiLSTM–Attention Model. Applied Sciences, 15(4), 2148–2148. https://doi.org/10.3390/app15042148

Giousmpasoglou, C., & Marinakou, E. (2021). Hotel internships and student satisfaction as key determinant to career intention. Journal of Tourism Research, 25, 42-67.

Gutiérrez-Pulido, H., & Orozco-Rodríguez, C. (2025). The contribution of professional internships to the academic development of engineering and science students: a case study. Frontiers in Education. Vol. 10. https://doi.org/10.3389/feduc.2025.1563361

Holderness, E., Cawkwell, P., Bolton, K., Pustejovsky, J., & Hall, M.-H. (2019). Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records. ArXiv (Cornell University). https://doi.org/10.18653/v1/w19-1915

Huda, A. K., Ramadhani, S. T. A., & Puri, F. M. (2025). A Comparative Study of Naive Bayes, Vader, and TextBlob Methods in Sentiment Analysis of ShopeeFood on Twitter. Brilliance: Research of Artificial Intelligence, 5(1), 26–36. https://doi.org/10.47709/brilliance.v5i1.5687

Hutto, C., & Gilbert, E. (2014). VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. Proceedings of the International AAAI Conference on Web

and Social Media, 8, 216-225. https://doi.org/10.1609/icwsm.v8i1.14550

Imanipour, M., Mirzaeipour, F., & Hazaryan, M. (2023). Effectiveness of feedback type on performance quality and satisfaction of nursing student: A comparative interventional study. Journal of Education and Health Promotion, 12(1), 324. https://doi.org/10.4103/jehp.jehp_1178_22

Imjai, N., Aujirapongpan, S., & Yaacob, Z. (2024). Impact of logical thinking skills and digital literacy on Thailand’s generation Z accounting students’ internship effectiveness: Role of self-learning capability. International Journal of Educational Research Open, 6, 100329–100329. https://doi.org/10.1016/j.ijedro.2024.100329

In, S., & Chanchamnan, S. (2026). Comparison of VADER and TextBlob Labeling for Sentiment Analysis Using Machine Learning and Deep Learning Models: A study on generative AI user experience. Acta Psychologica, 263, 106268. https://doi.org/10.1016/j.actpsy.2026.106268

Kingkaew,C., Supnithi, T., Theeramunkong, T., Morita, K., Tanaka, K., & Ikeda, M. (2019). A Learning Model to Improve Learning Outcome on Experiential Learning in a Multi-Phase Internship: a Case Study of the Internship Program of a Thai University. 8th International Congress on Advanced Applied Informatics (IIAI-AAI), Toyama, Japan, 209-214, https://doi: 10.1109/IIAI-AAI.2019.00049

Kolb, D. (1984). Experiential Learning: Experience as the Source of Learning and Development. New Jersey: Prentice Hall.

Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic reviews in sentiment analysis: a tertiary study. Artificial Intelligence Review, 54. https://doi.org/10.1007/s10462-021-09973-3

Linasari, Santioso. (2024). Industry Perspectives on Digital Competences Among MBKM Interns in Indonesia. Return Study of Management Economic and Bussines, 3(1), 41–60. https://doi.org/10.57096/return.v3i1.201

Loria, S. (2018). TextBlob Documentation. Release 0.15, 2.

Losekoot, E., Lasten, E., Lawson, A., & Chen, B. (2018). The development of soft skills during internships: The hospitality student’s voice. Research in Hospitality Management, 8(2), 155–159. https://doi.org/10.1080/22243534.2018.1553386

Ma’aly, A. N., Dita Pramesti, Ariadani Dwi Fathurahman, & Hanif Fakhrurroja. (2024). Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm. Information, 15 (11), 705–705. https://doi.org/10.3390/info15110705

Mao, Y., Liu, Q., & Zhang, Y. (2024). Sentiment analysis methods, applications, and challenges: A systematic literature review. Journal of King Saud University. Computer and Information Sciences/Magalat Gam’at Al-Malik Saud : Ùlm Al-Hasib Wa Al-Ma’lumat, 36(4), 102048–102048. https://doi.org/10.1016/j.jksuci.2024.102048

Nachar, N. (2008). The Mann-Whitney U: A Test for Assessing Whether Two Independent Samples Come from the Same Distribution. Tutorials in Quantitative Methods for Psychology, 4(1), 13–20. https://doi.org/10.20982/tqmp.04.1.p013

Nishiwaki, Y., Oshima, M., Hashimoto, K., & Tsuda, K. (2023). A Study of Sentiment Analysis based on Specific 6-emotion Category for Thai Language. Information Engineering Express, 9(2), 1. https://doi.org/10.52731/iee.v9.i2.774

O’Connor, H., & Bodicoat, M. (2016). Exploitation or opportunity? Student perceptions of internships in enhancing employability skills. British Journal of Sociology of Education, 38(4), 435–449. https://doi.org/10.1080/01425692.2015.1113855

Pantaruk, S., Khuadthong, B., Imjai, N., & Aujirapongpan, S. (2025). Fostering future-ready professionals: The impact of soft skills and internships on hospitality employability in Thailand. Social Sciences & Humanities Open, 11, 101371. https://doi.org/10.1016/j.ssaho.2025.101371

Patacsil, F., & Tablatin, C. L. (2017). Exploring the importance of soft and hard skills as perceived by IT internship students and industry: A gap analysis. Journal of Technology and Science Education, 7(3), 347–368. https://doi.org/10.3926/jotse.271

Post Reporters. (2024, December 2). Trends drive job openings. Bangkok Post. https://www.bangkokpost.com/business/general/2912500/trends-drive-job-openings

Rani, S., Singh, N., & Gulia, P. (2021). Survey of Tools and Techniques for Sentiment Analysis of Social Networking Data. International Journal of Advanced Computer Science and Applications, 12(4). https://doi.org/10.14569/ijacsa.2021.0120430

Reuters. (2024, September 30). Google to invest $1bn in Thai data centre, cloud infrastructure. Bangkok Post. https://www.bangkokpost.com/business/investment/2874952/google-to-invest-1bn-in-thai-data-centre-cloud-infrastructure

Santiago, T., & Gil, M. (2025). Preparing graduates for digital futures: critical insights from High-Tech internships in business administration. Education and Information Technologies. https://doi.org/10.1007/s10639-025-13751-x

Singkala, T. & Monpanthong, P. (2025). Internship Success In Thai Tourism: The Interplay Of Parental Support, Person-Organization Fit, And Internship Quality. Asian Administration and Management Review, Volume 8, Number 1, 1-14.

Suanpang, P., & Kaewyong, P. (2021). Sentiment Analysis with A Textblob Package Implications for Tourism. Journal of Management Information and Decision Sciences Volume 24, Special Issue 6, 202, 1-9

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics, 37(2), 267–307. https://doi.org/10.1162/coli_a_00049

Tang, D., Qin, B., & Liu, T. (2015). Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6), 292–303. https://doi.org/10.1002/widm.1171

Tirasriwat, A., Aryupong, M. ., Villanueva Vunnasiri, V. ., Chinnamma Ajoy , S. ., & Tansuwan, R. . (2024). A Qualitative Study of Factors Influencing Learning Outcomes and Student Satisfaction with Work-From-Home Internships. Journal of Business Administration The Association of Private Higher Education Institutions of Thailand, 13(1), 11–32.

U-senyang, S. (2024). Experiential Learning in Action: Analyzing Outcomes and Educational Implications. Journal of Education and Learning Reviews, 1(2), 13–28. https://doi.org/10.60027/jelr.2024.771

Venkit, P., Srinath, M., Gautam, S., Venkatraman, S., Gupta, V., Passonneau, R., & Wilson, S. (2023, December 1). The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis (H. Bouamor, J. Pino, & K. Bali, Eds.). ACLWeb; Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.848

Vithawat Surawattanasakul, Wuttipat Kiratipaisarl, & Penprapa Siviroj. (2024). Burnout and Quality of Work Life among Physicians during Internships in Public Hospitals in Thailand. Behavioral Sciences, 14(5), 361–361. https://doi.org/10.3390/bs14050361

Win Myint, P. Y., Lo, S. L., & Zhang, Y. (2024). Unveiling the dynamics of crisis events: Sentiment and emotion analysis via multi-task learning with attention mechanism and subject-based intent prediction. Information Processing & Management, 61(4), 103695. https://doi.org/10.1016/j.ipm.2024.103695

Wu, X., Lü, H., & Zhuo, S. (2015). Sentiment analysis for Chinese text based on emotion degree lexicon and cognitive theories. Journal of Shanghai Jiaotong University (Science), 20(1), 1–6. https://doi.org/10.1007/s12204-015-1579-x

Yeong, J., Kam, C., Lye, E., & Boo, S. (2024). The Impact of Internships on Graduates’ Employability: Employers’ Insights. Singapore Labour Journal, 03(01), 101–112. https://doi.org/10.1142/s281103152400007x

Yi, J., Ekapum Jiemwittayanukul, & Kusuma Yamgate. (2025). The Impact of Internship Experiences on the Employment of Students from Political and Law Vocational Colleges. 6(4), 99–110. https://doi.org/10.60027/ijsasr.2026.8055

Yonpiam, C. (2025, Feb 2). Experts urge action as declining birth rate set to hit Thai workforce. Bangkok Post. https://www.bangkokpost.com/thailand/general/2952626/experts-urge-action-as-declining-birth-rate-set-to-hit-thai-workforce

Youvan, D. C. (2024). Understanding sentiment analysis with VADER: a comprehensive overview and application. https://www.researchgate.net/publication

ดาวน์โหลด

เผยแพร่แล้ว

2026-06-30

รูปแบบการอ้างอิง

SUZUKI, H., Preedawongsakorn, B. ., Assavatesamongkol, N. ., ็Hemara R. ., Rattanawaropas, P. ., & Sereesuchat, S. . (2026). Comparison of Sentiment Analysis in Internship Feedback from Hospitality, Educational Institutions, and Other Industries: An Analysis Using TextBlob and VADER. วารสารมนุษยศาสตร์และสังคมศาสตร์ มหาวิทยาลัยเอเชียอาคเนย์, 10(1), 69–88. สืบค้น จาก https://so05.tci-thaijo.org/index.php/saujournalssh/article/view/286543

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