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Early prediction of the duration of protests using probabilistic Latent Dirichlet Allocation and Decision Trees | | Satyakama Paul
; Madhur Hasija
; Tshilidzi Marwala
; | | Date: |
18 Sep 2017 | | Abstract: | Protests and agitations are an integral part of every democratic civil
society. In recent years, South Africa has seen a large increase in its
protests. The objective of this paper is to provide an early prediction of the
duration of protests from its free flowing English text description. Free
flowing descriptions of the protests help us in capturing its various nuances
such as multiple causes, courses of actions etc. Next we use a combination of
unsupervised learning (topic modeling) and supervised learning (decision trees)
to predict the duration of the protests. Our results show a high degree (close
to 90%) of accuracy in early prediction of the duration of protests.We expect
the work to help police and other security services in planning and managing
their resources in better handling protests in future. | | Source: | arXiv, 1711.0462 | | Services: | Forum | Review | PDF | Favorites | |
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