Emotion based Voted Classier for Arabic Irony Tweet Identification
Nikita Kanwar, Rajesh Kumar Mundotiya, Megha Agarwal, Chandradeep Singh
Journal of Data Processing, Vol. 10, No. 2, 2020.
DOI: https://doi.org/10.6025/jdp/2020/10/2/52-56
Link: 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.