A Text Classifier Model for Categorizing Feed Contents Consumed by a Web Aggregator

This paper presents a method of using a Text Classifier to automatically categorize the content of web feeds consumed by a web aggregator. The pre-defined category of the feed to be consumed by the aggregator does not always match the content being consumed and categorizing the content using the pre-defined category of the feed curtails user experience as users would not see all the contents belonging to their category of interest. A web aggregator was developed and this was integrated with the SVM classifier to automatically categorize feed content being consumed. The experimental results showed that the text classifier performs well in categorizing the content of feed being consumed and it also affirmed the disparity in the pre-defined category of the source feed and appropriate category of the consumed content.

@article{Longe2014,
  title     = {A Text Classifier Model for Categorizing Feed Contents Consumed by a Web Aggregator},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {5},
  number    = {9},
  year      = {2014},
  publisher = {The Science and Information Organization},
  author    = {H.O.D. Longe and Fatai Salami},
  doi       = {10.14569/IJACSA.2014.050915},
  url       = {https://doi.org/10.14569/IJACSA.2014.050915}
}