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ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset

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arxiv 2011.00578 v3 pith:5G7CIBVY submitted 2020-11-01 cs.CL cs.LG

Basma Alharbi , Hind Alamro , Manal Alshehri , Zuhair Khayyat , Manal Kalkatawi , Inji Ibrahim Jaber , Xiangliang Zhang This is my paper · ORCID
classification cs.CLcs.LG
keywords arabicasaddatasetsentimentanalysisbenchmarkcompetitionprocess
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper provides a detailed description of a new Twitter-based benchmark dataset for Arabic Sentiment Analysis (ASAD), which is launched in a competition3, sponsored by KAUST for awarding 10000 USD, 5000 USD and 2000 USD to the first, second and third place winners, respectively. Compared to other publicly released Arabic datasets, ASAD is a large, high-quality annotated dataset(including 95K tweets), with three-class sentiment labels (positive, negative and neutral). We presents the details of the data collection process and annotation process. In addition, we implement several baseline models for the competition task and report the results as a reference for the participants to the competition.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English

    cs.CL 2024-12 conditional novelty 7.0 of 10

    BESSTIE is the first sentiment and sarcasm benchmark for three varieties of English, and models trained on it score lowest on Indian English.

  2. GLARE: Google Apps Arabic Reviews Dataset

    cs.CL 2024-12 conditional novelty 6.0 of 10

    GLARE releases 76 million Google Play reviews, 69 million in Arabic, from 9,980 Saudi Android apps, with descriptive statistics and engineered features.

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