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

2021/03/02 by Anshul Wadhawan, Wadhawan, Anshul · 1 citation
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Sentiment Analysis and Opinion Mining #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2103.01679

openalex publication_date 2021/03/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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• Ensembling Multilingual Transformers for Robust Sentiment Analysis of Tweets

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