AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets

• URL: http://arxiv.org/abs/2103.01679v1
• Date: Tue, 2 Mar 2021 12:33:50 GMT
• Title: AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets
• Authors: Anshul Wadhawan
• Abstract summary: One of the subtasks aims at developing a system that identifies whether a given Arabic tweet is sarcastic in nature or not. The other aims to identify the sentiment of the Arabic tweet. Our final approach was ranked seventh and fourth in the Sarcasm and Sentiment Detection subtasks respectively.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents our strategy to tackle the EACL WANLP-2021 Shared Task 2: Sarcasm and Sentiment Detection. One of the subtasks aims at developing a system that identifies whether a given Arabic tweet is sarcastic in nature or not, while the other aims to identify the sentiment of the Arabic tweet. We approach the task in two steps. The first step involves pre processing the provided ArSarcasm-v2 dataset by performing insertions, deletions and segmentation operations on various parts of the text. The second step involves experimenting with multiple variants of two transformer based models, AraELECTRA and AraBERT. Our final approach was ranked seventh and fourth in the Sarcasm and Sentiment Detection subtasks respectively.

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