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Rough Set and Entropy based Feature Selection for Online Forums Hotspot Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 117 - Number 10 |
| Year of Publication: 2015 |
| Authors: K. Nirmala Devi, V. Murali Bhaskaran |
10.5120/20593-3087
|
K. Nirmala Devi, V. Murali Bhaskaran . Rough Set and Entropy based Feature Selection for Online Forums Hotspot Detection. International Journal of Computer Applications. 117, 10 ( May 2015), 37-41. DOI=10.5120/20593-3087
Abstract
The exponential growth of web arouses much attention on public opinion. The rapid progress of online forums, micro blogs and new reports are having large volume of public opinion information. These are proving to be extremely valuable resources in helping to anticipate, detect and forecast societal events. But most of the online data is unstructured or semi structured and that is difficult to decipher automatically. Therefore, it is very much essential to analyze in time and understands the trends of their opinion correctly. The hotspot detection is one of the promising research areas in web mining and it helps to make appropriate decision in timely manner. Feature selection is an essential component in text categorization to identify the relevant features and reduces the dimensionality of data to gain the improved higher accuracy. The proposed system integrates rough set approach with entropy for detecting the online forums hotspot. The experimental results demonstrate that the proposed hybrid feature selection method outperforms with Naïve Bayes and Support Vector Machine based hotspot detection models.
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