Mental health and wellbeing chatbots are growing in popularity. Involving the end-user in creating these products is an important design consideration, to ensure technologies meet user needs and are easy to use. Extensive databases of app reviews provide rich data sources which can inform design, based on user feedback of apps already in existence. In this study, review mining was conducted on app reviews (n=20,461) across 7 mental health and wellbeing chatbots, focusing on the reviews that included the topics of design and user experience. The aim is to establish what user experience issues of mental wellbeing chatbots can be discovered. Natural language processing techniques were used to analyse reviews, and k-means clustering was applied to identify similar reviews based on content. These processes can be used to provide recommendations to designers of digital mental health technologies.
|Title of host publication||ECCE 2022 - Proceedings of the 33rd European Conference on Cognitive Ergonomics|
|Publisher||Association for Computing Machinery|
|Number of pages||5|
|Publication status||Published (in print/issue) - 4 Oct 2022|
|Event||33rd European Conference on Cognitive Ergonomics - Kaiserslautern, Germany|
Duration: 4 Oct 2022 → 7 Oct 2022
|Conference||33rd European Conference on Cognitive Ergonomics|
|Abbreviated title||ECCE 2022|
|Period||4/10/22 → 7/10/22|
Bibliographical noteFunding Information:
This study acknowledges the funding received from the Interreg Northern Periphery and Arctic Programme under grant number 345 (ChatPal).
© 2022 ACM.
- User reviews
- Text mining
- Mental health
- Natural language processing
- Well being
- Conversational user interfaces