Year: 2024
Type: conference-paper
Abstract: Automation can enhance performance and reduce human errors, but poorly designed interactive technologies can harm the user experience, leading to suboptimal outcomes for both systems and organisations. Social and technical subsystems are essential and interdependent and must be considered when designing organisational structures. Neglecting these aspects limits the potential for effective change. This paper explores the role of Aspect-Based Sentiment Analysis (ABSA) in improving user experience (UX) by addressing social factors in automated environments. As a subfield of Natural Language Processing (NLP), ABSA extracts insights tied to specific aspects from user feedback, allowing for targeted improvements that enhance both UX and system performance. With the growing reliance on unstructured data from sources like social media, we ask: Can ABSA play a key role in enhancing system design? Can it facilitate data collection and interpretation for the development of automated systems in large organisations? To assess ABSA’s effectiveness, we compiled a dataset of 8,060 tweets (2016–2023) related to automation and smart workplaces, using it as a case study. Six pre-trained or fine-tuned ABSA models were applied to analyse the data, focusing on dimensions such as usability, effectiveness, and innovation. Our findings highlight ABSA’s potential to guide improvements in socio-technical automated systems, and we propose recommendations for decision-makers, developers and researchers to identify strengths and weaknesses in automated systems using any type of user-generated comments. Finally, we present a lifecycle model using sentiment analysis to refine UX strategies, though further exploration of factors like trust and accessibility is needed. While the study offers valuable insights, further exploration of factors such as trust, accessibility, and ethical considerations is needed.
Institution: University of West London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7025219962
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