Name
Title | ||
|---|---|---|
A Tweets Mining Approach To Detection Of Critical Events Characteristics Using Random Forest |
Abstract | ||
|---|---|---|
During a natural disaster, while most people are overwhelmed, governmental agencies are in charge of public safety, and they must timely provide true directions, by quick and efficient analysis of massive amount of information. To guaranty decision making process, it is needed to extract and organize most important information as soon as possible to avoid adding more confusion. In this paper we show an automatic analysis of complex data such as text generated during an emergency event. An unsupervised and recent popular machine learning model called random forest is trained to uncover and organize predominant features from a large set of tweets; providing a hierarchy of main variables which might indicate rules and an approximation of how information flows during an unusual event. In our work we provide, firstly, a conversion process from text to numerical vectors for the training data; secondly, we introduce briefly random forest model; next, we expose an algorithm to adapt random forest to our problem; finally we show results for different configuration of the model and conclusions. |
Year | Venue | Keywords |
|---|---|---|
2014 | INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING | Random forest, main features detection |
Field | DocType | Volume |
Training set,Data mining,Confusion,Computer science,Complex data type,Natural disaster,Artificial intelligence,Hierarchy,Random forest,Machine learning,Decision-making | Journal | 5 |
Issue | ISSN | Citations |
2 | 2229-4678 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Enrique Gutierrez | 1 | 0 | 0.34 |
| Mohammad Reza Alsharif | 2 | 11 | 6.44 |
| Katsumi Yamashita | 3 | 0 | 0.68 |
| Mahdi Khosravy | 4 | 20 | 6.92 |