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Journal of Data Processing
 

Emotion based Voted Classier for Arabic Irony Tweet Identification
Nikita Kanwar, Rajesh Kumar Mundotiya, Megha Agarwal, Chandradeep Singh
Pratap Institute of Technology and Science, Sikar & Indian Institute of Technology (BHU), Varanasi
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.
Keywords: Voted Classifier, Emotion Feature Emotion based Voted Classier for Arabic Irony Tweet Identification
DOI:https://doi.org/10.6025/jdp/2020/10/2/52-56
Full_Text   PDF 344 KB   Download:   338  times
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