@inproceedings{3fc8e09f1d514646baa64b91d6701d8a,
title = "Fine-Grained Emotion Analysis of Arabic Tweets: A Multi-target Multi-label Approach",
abstract = "Emotion Analysis (EA) is the task of determining the emotion of a given piece of text. This is an important task with many applications especially when applied to tweets. However, existing work take a rather coarse-grained approach by assuming that each tweet has a single emotion and that this emotion has no intensity. In this work, we take a fine-grained approach by considering cases where a single tweet may have several emotions (multi-label) each with possibly different intensity (multi-target). Moreover, unlike existing work, which consider languages such as English and Chinese, we focus on the Arabic language, a severely under-studied language despite its importance. We build the first dataset (to the best of our knowledge) of Arabic tweets annotated for emotion analysis as a multi-label multi-target problem. Two human experts participated in the annotation process and Cohen's Kappa measure was used to determine their concordance.",
keywords = "Arabic Tweets, Emotion Analysis, Multi-Target Multi-Label Approach",
author = "Omar Badarneh and Mahmoud Al-Ayyoub and Nouh Alhindawi and Tawalbeh, \{Loai A.\} and Yaser Jararweh",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 12th IEEE International Conference on Semantic Computing, ICSC 2018 ; Conference date: 31-01-2018 Through 02-02-2018",
year = "2018",
month = apr,
day = "9",
doi = "10.1109/ICSC.2018.00070",
language = "English",
series = "Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "340--345",
booktitle = "Proceedings - 12th IEEE International Conference on Semantic Computing, ICSC 2018",
address = "United States",
}