dc.contributor.author | Li, H | |
dc.contributor.author | Jadidi, Z | |
dc.contributor.author | Chen, J | |
dc.contributor.author | Jo, J | |
dc.date.accessioned | 2020-04-27T01:19:33Z | |
dc.date.available | 2020-04-27T01:19:33Z | |
dc.date.issued | 2019 | |
dc.identifier.isbn | 9783319784519 | |
dc.identifier.issn | 2194-5357 | |
dc.identifier.doi | 10.1007/978-3-319-78452-6_25 | |
dc.identifier.uri | http://hdl.handle.net/10072/393401 | |
dc.description.abstract | 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. | |
dc.description.peerreviewed | Yes | |
dc.publisher | Springer | |
dc.relation.ispartofconferencename | 5th International Conference on Robot Intelligence Technology and Applications (RiTA 2017) | |
dc.relation.ispartofconferencetitle | Advances in Intelligent Systems and Computing | |
dc.relation.ispartofdatefrom | 2017-12-13 | |
dc.relation.ispartofdateto | 2017-12-15 | |
dc.relation.ispartoflocation | Deajeon, Korea | |
dc.relation.ispartofpagefrom | 291 | |
dc.relation.ispartofpagefrom | 8 pages | |
dc.relation.ispartofpageto | 298 | |
dc.relation.ispartofpageto | 8 pages | |
dc.relation.ispartofvolume | 751 | |
dc.subject.fieldofresearch | Artificial intelligence | |
dc.subject.fieldofresearchcode | 4602 | |
dc.title | The use of machine learning for correlation analysis of sentiment and weather data | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Li, H; Jadidi, Z; Chen, J; Jo, J, The use of machine learning for correlation analysis of sentiment and weather data, Advances in Intelligent Systems and Computing, 2019, 751, pp. 291-298 | |
dc.date.updated | 2020-04-27T01:17:06Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © Springer International Publishing AG, part of Springer Nature 2019. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Jo, Jun | |
gro.griffith.author | Jadidi, Zahra | |