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

Smart Border Control (SBC) technologies became a prominent topic in recent years when the European 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 travellers. 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.

NTNU Partner has submitted in the Northern Lights Deep Learning Conference (NLDL), a study proposing 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.

Additional information about the NLDL Conference can be found here!

Conference agenda, information about METICOS Participation as well as the actual study will be available shortly!