  Emotion recognition is a crucial task in Natural Language Processing (NLP)
that enables machines to comprehend the feelings conveyed in the text. The
applications of emotion recognition are diverse, including mental health
diagnosis, student support, and the detection of online suspicious behavior.
Despite the substantial amount of literature available on emotion recognition
in various languages, Arabic emotion recognition has received relatively little
attention, leading to a scarcity of emotion-annotated corpora. This paper
presents the ArPanEmo dataset, a novel dataset for fine-grained emotion
recognition of online posts in Arabic. The dataset comprises 11,128 online
posts manually labeled for ten emotion categories or neutral, with Fleiss'
kappa of 0.71. It targets a specific Arabic dialect and addresses topics
related 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-19
pandemic from three sources: Twitter, YouTube, and online newspaper comments
between March 2020 and March 2022. Upon collection of the online posts, each
one underwent a semi-automatic classification process using a lexicon of
emotion-related terms to determine whether it belonged to the neutral or
emotional category. Subsequently, manual labeling was conducted to further
categorize the emotional data into fine-grained emotion categories.
