Authors:
Jaromir Salamon
and
Roman Moucek
Affiliation:
University of West Bohemia, Czech Republic
Keyword(s):
Sentiment Extraction, Sentiment Analysis, Body Sensors, Machine Learning, Experiment.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Cardiovascular Technologies
;
Cloud Computing
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Devices
;
e-Health
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Knowledge-Based Systems
;
Pattern Recognition and Machine Learning
;
Physiological Computing Systems
;
Platforms and Applications
;
Symbolic Systems
;
Wearable Sensors and Systems
Abstract:
The Internet of Things world brings to our lives many opportunities to monitor our daily activities by collecting data from various devices. Complementary to it, the data expressing opinions, suggestions, interpretations, contradictions, and uncertainties are more accessible within variety of online resources. This paper deals with collection and analysis of hard data representing the number of steps and soft data representing the sentiment of participants who underwent a pilot experiment. The paper defines outlines of the problem and presents possible sources of reliable data, sentiment evaluation, sentiment extraction using machine learning methods, and links between the data collected from IoT devices and sentiment expressed by the participant in a textual form. Then the results provided by using inferential statistics are presented. The paper is concluded by discussion and summarization of results and future work proposals.