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Cited by 60Sentiment Analysis and Opinion MiningEmotion and Mood RecognitionMental Health via Writing

EMA at SemEval-2018 Task 1: Emotion Mining for Arabic

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Badaro, Gilbert, El Jundi, Obeida, Khaddaj, Alaa, Maarouf, Alaa, Kain, Raslan, Hajj, Hazem, El-Hajj, Wassim·2018·Proceedings of The 12th International Workshop on Semantic Evaluation
DOI 10.18653/v1/s18-1036
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Abstract

While significant progress has been achieved for Opinion Mining in Arabic (OMA), very limited efforts have been put towards the task of Emotion mining in Arabic.In fact, businesses are interested in learning a fine-grained representation of how users are feeling towards their products or services.In this work, we describe the methods used by the team Emotion Mining in Arabic (EMA), as part of the SemEval-2018 Task 1 for Affect Mining for Arabic tweets.EMA participated in all 5 subtasks.For the five tasks, several preprocessing steps were evaluated and eventually the best system included diacritics removal, elongation adjustment, replacement of emojis by the corresponding Arabic word, character normalization and light stemming.Moreover, several features were evaluated along with different classification and regression techniques.For the 5 subtasks, word embeddings feature turned out to perform best along with Ensemble technique.EMA achieved the 1 st place in subtask 5, and 3 rd place in subtasks 1 and 3.

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  • Most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions.

    “However, most existing models for MLEC do not capture emotion-specific associations that can be useful for the classification of emotions [7], [9], [10].”

    From Emotion Classification With Visibility Graphs (2025) · cites this paper as [10]

    The Related Work sections describe approaches to emotion classification, and §4.3 compares models used by the authors. Neither passage says that most existing MLEC models fail to capture emotion-specific associations. Searches for that phrase and for associations between emotions found no passage addressing the claim. (LLM, conf=0.72)

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Cited by

Sorted by citations

Authors
Badaro, G., El Jundi, O., Khaddaj, A., Maarouf, A., Kain, R., Hajj, H., & El-Hajj, W.
Affiliations
American University of Beirut (LB)
ORCID
Venue
Proceedings of The 12th International Workshop on Semantic Evaluation
Publisher
Association for Computational Linguistics
Published
2018
Pages
236-244
DOI
10.18653/v1/s18-1036
OpenAlex
W2807136170
Cited by
60
References
3
Citations by year
2026: 5 · 2025: 2 · 2024: 5 · 2023: 10 · 2022: 5 · 2021: 13 · 2020: 10 · 2019: 7 · 2018: 3
FWCI
3.35
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
International Workshop on Semantic Evaluation (SemEval) (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Keywords
emotion analysis, Arabic text, tweets, sentiment analysis, text preprocessing, word embeddings, ensemble classification, regression analysis

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