Name
Abstract | ||
|---|---|---|
Social media is increasingly being used during crises. This makes it possible for crisis responders to collect and process crisis-related user generated content to allow for improved situational awareness. We describe a methodology for collecting a large number of relevant tweets and annotating them with emotional labels. This methodology has been used for creating a training data set consisting of manually annotated tweets from the Sandy hurricane. Those tweets have been utilized for building machine learning classifiers able to automatically classify new tweets. Results show that a support vector machine achieves the best results (60% accuracy on the multi-classification problem). |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISI.2013.6578782 | 2013 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS: BIG DATA, EMERGENT THREATS, AND DECISION-MAKING IN SECURITY INFORMATICS |
Keywords | Field | DocType |
niobium,learning artificial intelligence,cognition,support vector machine,media,support vector machines,social media,training data,hurricanes,accuracy | Training set,User-generated content,Data mining,Social media,Active learning (machine learning),Situation awareness,Computer science,Support vector machine | Conference |
Citations | PageRank | References |
6 | 0.46 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joel Brynielsson | 1 | 173 | 20.20 |
| Fredrik Johansson | 2 | 95 | 7.18 |
| Anders Westling | 3 | 6 | 0.46 |