Towards Understanding of User Perceptions for Smart Border Control Technologies using a Fine-Tuned Transformer Approach

Authors

  • Sarang Shaikh Dept. of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)
  • Sule Yildirim Yayilgan Dept. of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)
  • Erjon Zoto Dept. of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)
  • Mohamed Abomhara Dept. of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)

DOI:

https://doi.org/10.7557/18.6292

Keywords:

deep learning, machine learning, transformer models, transfer learning, NLP, border control technologies, sentiment analysis, user perceptions, BCPs

Abstract

Smart Border Control (SBC) technologies became a hot topic in recent years when the European Union (EU) Commission announced the Smart Borders Package to improve the efficiency and security of the border crossing points (BCPs). Although, BCPs technologies have potential benefits in terms of enabling traveller' data processing, they still lead to acceptability and usability challenges when used by travelers. Success of technologies depends on user acceptance. Sentiment analysis is one of the primary techniques to measure user acceptance. Although, there exists variety of studies in literature where sentiment analysis has been used to understand user acceptance in different domains. To the best of our knowledge, there is no study where sentiment analysis has been used for measuring the user acceptance of SBC technologies. Thus, in this study, we propose a fine-tuned transformer model along with an automatic sentiment labels generation technique to perform sentiment analysis as a step towards getting insights into user acceptance of BCPs technologies. The results obtained in this study are promising; given the condition that there is no training data available from BCPs. The proposed approach was validated against IMDB reviews dataset and achieved weighted F1-score of 79% for sentiment analysis task.

Author Biographies

Sarang Shaikh, Dept. of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)

Sarang Shaikh is currently associated with the Department of Information Security and Communication Technology at Norwegian University of Science and Technology (NTNU), Norway, as a PhD Candidate since Feb, 2021. He obtained his Masters degree from Sukkur IBA University, Pakistan, in Computer Science in 2020. His research interests are towards applied research in the field of artificial intelligence, NLP, machine learning, deep learning, and learning technologies. He is the author of several papers published in international journals and has served as a reviewer for IEEE Access. 

Sule Yildirim Yayilgan, Dept. of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU)

Professor Sule Yildirim Yayilgan works at the Department of Information Security and Communication Technology (IIK), NTNU since 2009. She received a MSc. degree in Computer Engineering in 1995, and PhD in Artificial Intelligence and Computer Science in 2002. She has worked more than 25 years in academia teaching (a copy of universitetpedagogikk certificate is in the CV) and doing research and served as Head of the Department between 2005-2009. She has participated in projects funded by EU Horizon 2020, Eurostars, Erasmus+ programs, the Research Council of Norway, the Regional Research Council of Norway and the Ministry of Foreign Affairs, Norway. She belongs to the Center for Cyber Information Security (ccis.no) and she is leading the research group MR PET: Multidisciplinary Research group on Privacy and data protEcTion. She has been supervising students at different academic levels and has been publishing more than 100 journal and conference papers.

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Published

2022-03-28

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Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022

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Copyright (c) 2022 Sarang Shaikh, Sule Yildirim Yayilgan, Erjon Zoto, Mohamed Abomhara

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