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Emotion recognition is a crucial task in Natural Language Processing (NLP)that enables machines to comprehend the feelings conveyed in the text. Theapplications of emotion recognition are diverse, including mental healthdiagnosis, student support, and the detection of online suspicious behavior.Despite the substantial amount of literature available on emotion recognitionin various languages, Arabic emotion recognition has received relatively littleattention, leading to a scarcity of emotion-annotated corpora. This paperpresents the ArPanEmo dataset, a novel dataset for fine-grained emotionrecognition of online posts in Arabic. The dataset comprises 11,128 onlineposts manually labeled for ten emotion categories or neutral, with Fleiss'kappa of 0.71. It targets a specific Arabic dialect and addresses topicsrelated to the COVID-19 pandemic, making it the first and largest of its kind.Python's packages were utilized to collect online posts related to the COVID-19pandemic from three sources: Twitter, YouTube, and online newspaper commentsbetween March 2020 and March 2022. Upon collection of the online posts, eachone underwent a semi-automatic classification process using a lexicon ofemotion-related terms to determine whether it belonged to the neutral oremotional category. Subsequently, manual labeling was conducted to furthercategorize the emotional data into fine-grained emotion categories.

ArPanEmo: An Open-Source Dataset for Fine-Grained Emotion Recognition in Arabic Online Content during COVID-19 Pandemic