@article{3001,
  author = {Nikita Kanwar, Rajesh Kumar Mundotiya, Megha Agarwal, Chandradeep Singh},
  title = {Emotion based Voted Classier for Arabic Irony Tweet Identification},
  journal = {Journal of Data Processing},
  year = {2020},
  volume = {10},
  number = {2},
  doi = {https://doi.org/10.6025/jdp/2020/10/2/52-56},
  url = {https://www.dline.info/jdp/fulltext/v10n2/jdpv10n2_2.pdf},
  abstract = {In this paper, we have worked on irony detection in the Arabic language, a task which is organized by FIRE 2019. The tweets have been preprocessed and tokenized to extract the frequency-based, emotion-based features. These features are used to irony identification using the voted classier. The F-score of our proposed approach is 0.807 and the topranking developed method having F-score of .037, so the difference between F-score makes our approach better.},
}