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The purpose of this study is to identify and assess new risks in construction projects that use 3D printing.

A mixed approach of both qualitative and quantitative methods was used. Literature review was conducted to extract 30 risks of 3D printing in construction. A survey was then developed to assess the probability and impact of these risks. In total, 37 respondents, who have experience and/or knowledge of 3D printing, completed the survey. The risk priority was calculated using a fuzzy logic approach. The main benefit of the proposed model is being able to use numerical and linguistic data in the risk assessment model.

The results show that the main risks, in terms of priority, are lack of codes and regulations for 3D printing in construction, delay in government approvals, shortage in labour skilled in 3D printed construction, lack of knowledge and information of 3D printed design concepts and changes in 3D construction codes and regulations.

This paper fills an identified gap in the literature related to 3D printing in construction and provides insights into the key risks affecting this disruptive technology.

This study examines the role of online customer reviews through text mining and sentiment analysis to improve customer satisfaction across various services within the UK banking sector. Additionally, the study analyses sentiment trends over a five-year period.

Using DistilBERT and Support Vector Machine algorithms, customer sentiments were assessed through an analysis of 20,137 Trustpilot reviews of HSBC, Santander, and Tesco Bank from 2018 to 2023. Data pre-processing steps were implemented to ensure data integrity and minimize noise.

Both positive and negative sentiments provide valuable insights. The results indicate a high prevalence of negative sentiments related to customer service and communication, with HSBC and Santander receiving 90.8% and 89.7% negative feedback, respectively, compared to Tesco Bank’s 66.8%. Key areas for improvement include HSBC’s credit card services and call center efficiency, which experienced increased negative feedback during the COVID-19 pandemic. The findings also demonstrate that DistilBERT excelled in categorizing reviews, while the SVM model, when combined with customer ratings, achieved 96% accuracy in sentiment analysis.

This study focuses on UK bank consumers of HSBC, Santander, and Tesco Bank. A multi-country or cross-cultural study may further enhance our understanding of the approaches and findings.

Online customer reviews become more informative when categorised by service sector. To enhance customer satisfaction, bank managers should pay attention to both positive and negative reviews, and track trends over time.

The uniqueness of this study lies in its exploration of the importance of categorisation in text-mining-based sentiment analysis, its focus on the influence of both positive and negative sentiments, and its emphasis on tracking sentiment trends over time.