| Autor: |
Pogrebnyakov, Nicolai, Maldonado, Edgar |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Proceedings of the 5th IEEE International Conference on Big Data, Boston, MA, USA, December 11-14, 2017 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/BigData.2017.8258464 |
| Popis: |
Classification of social media posts in emergency response is an important practical problem: accurate classification can help automate processing of such messages and help other responders and the public react to emergencies in a timely fashion. This research focused on classifying Facebook messages of US police departments. Randomly selected 5,000 messages were used to train classifiers that distinguished between four categories of messages: emergency preparedness, response and recovery, as well as general engagement messages. Features were represented with bag-of-words and word2vec, and models were constructed using support vector machines (SVMs) and convolutional (CNNs) and recurrent neural networks (RNNs). The best performing classifier was an RNN with a custom-trained word2vec model to represent features, which achieved the F1 measure of 0.839. |
| Databáze: |
arXiv |
| Externí odkaz: |
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