TY - GEN
T1 - The use of machine learning for correlation analysis of sentiment and weather data
AU - Li, Hu
AU - Jadidi, Zahra
AU - Chen, Jinyan
AU - Jo, Jun
N1 - Funding Information:
Acknowledgements This project was partly funded through a National Environment Science Program (NESP) fund, within the Tropical Water Quality Hub (Project No: 2.3.2).
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2019.
PY - 2019
Y1 - 2019
N2 - The development of the Internet of Things (IoT) drives us to confront, manage and analyse massive and complicated data generated from various sensors. Also, social media have rapidly become very popular and can be considered as important source of data. Twitter on average generates about 6,000 tweets every second, which total to over 500 million tweets per day. Facebook has over 2 billion monthly active users. The individual posts may be trivial, however, the accumulated big data can provide diverse valuable information, which can be also correlated with IoT and enable human sentiment identification of the environment changes. This work proposes a machine learning model for correlation analysis and prediction of weather conditions and social media posts. In experimental evaluation we demonstrate that the Big Data analysis approach and machine learning techniques can be used to analyse and predict sentiment of different weather conditions.
AB - The development of the Internet of Things (IoT) drives us to confront, manage and analyse massive and complicated data generated from various sensors. Also, social media have rapidly become very popular and can be considered as important source of data. Twitter on average generates about 6,000 tweets every second, which total to over 500 million tweets per day. Facebook has over 2 billion monthly active users. The individual posts may be trivial, however, the accumulated big data can provide diverse valuable information, which can be also correlated with IoT and enable human sentiment identification of the environment changes. This work proposes a machine learning model for correlation analysis and prediction of weather conditions and social media posts. In experimental evaluation we demonstrate that the Big Data analysis approach and machine learning techniques can be used to analyse and predict sentiment of different weather conditions.
UR - http://www.scopus.com/inward/record.url?scp=85048241502&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-78452-6_25
DO - 10.1007/978-3-319-78452-6_25
M3 - Conference article published in proceeding or book
AN - SCOPUS:85048241502
SN - 9783319784519
T3 - Advances in Intelligent Systems and Computing
SP - 291
EP - 298
BT - Robot Intelligence Technology and Applications 5 - Results from the 5th International Conference on Robot Intelligence Technology and Applications
A2 - Myung, Hyun
A2 - Xu, Weiliang
A2 - Jung, Jin-Woo
A2 - Choi, Han-Lim
A2 - Kim, Jong-Hwan
A2 - Kim, Junmo
A2 - Matson, Eric T
PB - Springer-Verlag
T2 - 5th International Conference on Robot Intelligence Technology and Applications, RiTA 2017
Y2 - 13 December 2017 through 15 December 2017
ER -