Short Text Topic Modeling to Identify Trends on Wearable Bio-sensors in Different Media TypesShow others and affiliations
2019 (English)In: Proceedings - 6th International Symposium on Computational and Business Intelligence, ISCBI 2018, IEEE Computer Society, 2019, p. 89-93Conference paper, Published paper (Refereed)
Abstract [en]
The technology and techniques for bio-sensors are rapidly evolving. Accordingly, there is significant business interest to identify upcoming technologies and new targets for the near future. Text information from internet reflects much of the recent information and public interests that help to understand the trend of a certain field. Thus, we utilize Dirichlet process topic modeling on different media sources containing short text (e.g., blogs, news) which is able to self-adapt the learned topic space to the data. We share the observations from the domain experts on the results derived from topic modeling on wearable biosensors from multiple media sources over more than eight years. We analyze the topics on wearable devices, forecast and market analysis, and bio-sensing techniques found from our method.
Place, publisher, year, edition, pages
IEEE Computer Society, 2019. p. 89-93
Keywords [en]
Bayesian non-parametrics, Bio-sensor, short text, topic modeling, wearable, Biosensors, Information analysis, Bayesian nonparametrics, Dirichlet process, Market analysis, Short texts, Text information, Wearable devices, Wearable sensors
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16746DOI: 10.1109/ISCBI.2018.00027ISI: 000462379700017Scopus ID: 2-s2.0-85063041846ISBN: 978-1-5386-9450-3 (electronic)ISBN: 978-1-5386-9451-0 (print)OAI: oai:DiVA.org:his-16746DiVA, id: diva2:1302771
Conference
ISCBI 2018 : 2018 6th International Symposium on Computational and Business Intelligence. Basel, Switzerland August 22 - 29 2018
2019-04-052019-04-052020-06-18Bibliographically approved