Detection of Sentiment Provoking Events in Social Media Daou, Hoda 2019-01-03T00:02:48Z 2019-01-03T00:02:48Z 2019-01-08
dc.description.abstract Social media has become one of the main sources of news and events due to its ability to disseminate and propagate information very fast and to a large population. Social media platforms are widely accessible to the population making it difficult to extract relevant information from the huge amount of posted data. In our study, we propose an algorithm that automatically detects events using strong sentiment classification and appropriate clustering techniques. We focus our study on a specific type of events that triggers strong sentiment among the public. Results show that the suggested methodology is able to identify important events, such as a mass shooting and plane crash, using a generalized and simple approach.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2019.279
dc.identifier.isbn 978-0-9981331-2-6
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject Digital and Social Media
dc.subject Classification, Events, Machine Learning, Social Media
dc.title Detection of Sentiment Provoking Events in Social Media
dc.type Conference Paper
dc.type.dcmi Text
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