Towards Understanding of User Perceptions for Smart Border Control Technologies using a Fine-Tuned Transformer Approach
Introduction and Background
SBC technologies are fully or semi-automated technologies with the purpose of verifying traveller’s identity without any human (i.e., border guard) intervention . Despite the potential benefits of the SBC technologies at BCPs, users (i.e. travellers) perceptions of acceptability of the technologies may change with actual experience of users with them.
We have a focus on finding out the reasons for:
- why acceptance of SBC technologies depend on user perceptions,
- changes in user perceptions for SBC technologies, and
- changes in user perceptions after users have actual experience with SBC technologies
through questionnaires and social media data analysis
Table 1. State-of-the-art studies for extracting user perceptions from social media data
|Authors||Social Media Platform||Data||Perceptions||Technique||Type||Results|
|Khan et. al. ||Yelp||Yelp Challenge Dataset (2835065 user reviews)||Rating perceptions to restaurants, ecommerce||Users Rating Classification using Machine Learning (NaiveBayes, RandomForest, DecisionTree, SVM, KNN, MLP)||Supervised||96% NaiveBayes|
|Xuan et. al. ||Facebook Posts (37426 in health, technology, travel, finance, sports)||Users interests and trends (i.e. health, tech, travel, sports)||Users Trends Classification using Machine Learning (NaiveBayes, DecisionTree, SVM), Deep Learning (CNN)||Supervised||91% CNN|
|Alshamrani et. al. ||Tweets (26 million b/w 01/10/2019 and 30/04/2020)||users behaviours and trends before and after COVID-19||Deep Learning for Sentiment Analysis (DNN, CNN, LSTM, BERT), LDA for Topic Modelling||Supervised||87% BERT|
|Rosario et. al. ||Twitter and surveys||N/A||users experiences and perceptions for e- learning tools||Deep Learning (CNN, BiLSTM, Transformers), Google, Microsoft APIs||Supervised||61.7% Transformer|
|Molinari et. al. ||Tweets (13800 related to URBAN- GEO BIG DATA sensing)||Investigate citizens perceptions of mobility services for urban geo sensing||Machine Learning (NaiveBayes)||Supervised||N/A|
Methods and Materials
This study analyzed user tweets in order to understand user perceptions for SBC technologies by combining BERT model with the Flair labelling technique.
Advantages of the proposed system
– automatic sentiment labels generation when there is no labelled data exists
– applying the proposed study for extracting sentiments to understand user perceptions for SBC technologies
The highest performing model was the combination of Flair and BERT with 79% accuracy on the IMDB test dataset
The proposed approach can be customized for use in other domains where raw data exists, however, there is no processed and labelled data available.
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Sarang Shaikh1, Sule Yildirim Yayilgan1, Erjon Zoto1, Mohamed Abomhara1 1Dept. Of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU), Gjøvik, Norway