Arabic Location Named Entity Recognition for Tweets using a Deep Learning Approach

Social media sites like Twitter have emerged in recent years as a major data source utilized in a variety of disciplines, including economics, politics, and scientific study. To extract pertinent data for decision-making and behavioral analysis, one can use Twitter data. To extract event location names and entities from colloquial Arabic texts using deep learning techniques, this study proposed Named Entity Recognition (NER) and Linking (NEL) models. Google Maps was also used to obtain up-to-date details for each extracted site and link them to the geographical coordination. Our method was able to predict 40% and 48% of the locations of tweets at the regional and city levels, respectively, while the F-measure was able to reliably identify and detect 63% of the locations of tweets at a single Point of Interest.

@article{Alzaidi2022,
  title     = {Arabic Location Named Entity Recognition for Tweets using a Deep Learning Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {12},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Bedour Swayelh Alzaidi and Yoosef Abushark and Asif Irshad Khan},
  doi       = {10.14569/IJACSA.2022.0131211},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131211}
}