In recent years, sentiment mining has been used within politics and social media to understand the emotions and opinions that people have about society and different political groups to help bring change. However, in the user centered design process, a main component that helps bring change is the use of user feedback and there has been little research in terms of adopting sentiment mining to assist with this and the user experience of an application. This research paper implements the use of sentiment mining on user survey feedback from Jupyter Notebook and has identified different components of the user experience that caters for novice and expert users independently. Recommendations from this research will help Jupyter Notebook create a user experience that caters to novice and expert users and their workflow needs. This initial analysis has identified that sentiment mining provides important and subtle enhancements for user experience that other tools and techniques do not provide. Therefore, this is a worthwhile method of user experience assessment to foster to enhance a digital user experience.
|Title of host publication||2022 8th International HCI and UX Conference in Indonesia (CHIuXiD)|
|Number of pages||6|
|ISBN (Electronic)||978-1-6654-7664-5, 978-1-6654-7663-8|
|Publication status||Published online - 13 Jan 2023|
|Event||HCI and UX Conference in Indonesia (CHIuXiD), 2022 8th International - , Indonesia|
Duration: 19 Nov 2022 → 19 Nov 2022
|Conference||HCI and UX Conference in Indonesia (CHIuXiD), 2022 8th International|
|Period||19/11/22 → 19/11/22|
Bibliographical noteThe authors acknowledge the support of the European Commission on the H2020 project MIDAS (G.A. nr. 727721) and Department of the Economy Northern Ireland.
ACKNOWLEDGMENT The authors acknowledge the support of the European Commission on the H2020 project MIDAS (G.A. nr. 727721) and Department of the Economy Northern Ireland.
© 2022 IEEE.
- cohort analysis
- sentiment classification
- user experience
- machine learning
- jupyter notebook
- Human computer interaction
- Sentiment analysis
- Social networking (online)
- User centered design