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

Badaro, Gilbert; Jundi, Obeida El; Khaddaj, Alaa; Maarouf, Alaa; Kain, Raslan; Hajj, Hazem; El-Hajj, Wassim

doi:10.18653/v1/s18-1036

Cited by 43 publications

(38 citation statements)

References 41 publications

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“…In SemEval 2018 Task 1: Affect in Tweets [39], labeled data from English, Arabic, and Spanish tweets are created for each task. Badaro et al [40] achieved the best result in the SemEval 2018 emotion classification subtask for the Arabic language. Features that they used were word embeddings from AraVec, and emotion features extracted from ArSEL [41] and NRC emotion lexicon.…”

Section: Related Workmentioning

confidence: 99%

Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

Wang

Kou

Sugumaran

et al. 2021

IEEE Trans. Cybern.

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Emotion analysis has been attracting researchers' attention. Most previous works in the artificial-intelligence field focus on recognizing emotion rather than mining the reason why emotions are not or wrongly recognized. The correlation among emotions contributes to the failure of emotion recognition. In this article, we try to fill the gap between emotion recognition and emotion correlation mining through natural language text from Web news. The correlation among emotions, expressed as the confusion and evolution of emotion, is primarily caused by human emotion cognitive bias. To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural-network models are presented. The emotion confusion law is extracted through an orthogonal basis. The emotion evolution law is evaluated from three perspectives: one-step shift, limited-step shifts, and shortest path transfer. The method is validated using three datasets: 1) the titles; 2) the bodies; and 3) the comments of news articles, covering both objective and subjective texts in varying lengths (long and short). The experimental results show that in subjective comments, emotions are easily mistaken as anger. Comments tend to arouse emotion circulations of love-anger and sadness-anger. In objective news, it is easy to recognize text emotion as love and cause fear-joy circulation. These findings could provide insights for applications regarding affective interaction, such as network public sentiment, social media communication, and human-computer interaction.

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Section: Related Workmentioning

confidence: 99%

Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

Wang

Kou

Sugumaran

et al. 2021

IEEE Trans. Cybern.

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“…The number of participants in the SemEval-2018 competition for emotion recognition in Arabic compared to the number of English participants was low. Of the eleven participants, only five achieved results higher than the baseline, and of those five, only Badaro et al [2], Mulki et al [3], and Abdullah and Shaikh [4] submitted a paper describing their systems.…”

Section: Related Workmentioning

confidence: 99%

“…Badaro et al [2] proposed a learning-based model for multi-label emotion recognition and tested several features, including n-grams, affect lexicons, sentiment lexicon, and word embeddings from AraVec [5] and FastText [6]. AraVec embeddings outperformed the other features.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Model for Emotion Recognition in Arabic Text

Alswaidan

Menaï

2020

IEEE Access

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In recent years, research into developing state-of-the-art models for Arabic natural language processing tasks has gained momentum. These models must address the added difficulties related to the nature and structure of the Arabic language. In this paper, we propose three models, a human-engineered feature-based (HEF) model, a deep feature-based (DF) model, and a hybrid of both models (HEF+DF) for emotion recognition in Arabic text. We evaluated the performance of the proposed models on the SemEval-2018, IAEDS, and AETD datasets by comparing the performances of those models on each emotion label. We also compared the model performances with those of other state-of-the-art models. The results show that the HEF+DF model outperformed the DF and HEF models on all datasets. The DF model performed better than the HEF model on the SemEval-2018 and AETD datasets, while the HEF model performed better than the DF model on the IAEDS dataset. The HEF+DF model outperformed the state-of-the-art models in terms of accuracy, weighted-average precision, weighted-average recall, and weighted-average F-score on the AETD dataset and in terms of accuracy, macro-averaged precision, macro-averaged recall, and macroaveraged F-score on the IAEDS dataset. It also achieved the best macro-averaged F-score and the second-best Jaccard accuracy and micro-averaged F-score on the SemEval-2018 dataset. INDEX TERMS Arabic natural language processing, deep learning, emotion recognition, small dataset.

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“…To address the morphological richness and orthographic ambiguity of the Arabic language, proposed the first Arabic Sentiment Treebank (ARSENTB) and trained RNTN to outperform AROMA. AraVec word embeddings (Soliman et al, 2017) were utilized by (Badaro et al, 2018) to win SemEval 2018 (Mohammad et al, 2018). (Dahou et al, 2016) and (Dahou et al, 2019) investigated a CNN architecture similar to (Kim, 2014) trained on locally trained word embeddings to achieve significant results.…”

Section: Arabic Sentiment Analysismentioning

confidence: 99%

hULMonA: The Universal Language Model in Arabic

ElJundi¹,

Antoun²,

Droubi³

et al. 2019

Proceedings of the Fourth Arabic Natural Language Processing Workshop

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Arabic is a complex language with limited resources which makes it challenging to produce accurate text classification tasks such as sentiment analysis. The utilization of transfer learning (TL) has recently shown promising results for advancing accuracy of text classification in English. TL models are pre-trained on large corpora, and then fine-tuned on taskspecific datasets. In particular, universal language models (ULMs), such as recently developed BERT, have achieved state-of-the-art results in various NLP tasks in English. In this paper, we hypothesize that similar success can be achieved for Arabic. The work aims at supporting the hypothesis by developing the first Universal Language Model in Arabic (hUL-MonA-meaning our dream), demonstrating its use for Arabic classifications tasks, and demonstrating how a pre-trained multilingual BERT can also be used for Arabic. We then conduct a benchmark study to evaluate both ULM successes with Arabic sentiment analysis. Experiment results show that the developed hULMonA and multilingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.

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

Cited by 43 publications

References 41 publications

Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

Emotion Correlation Mining Through Deep Learning Models on Natural Language Text

Hybrid Feature Model for Emotion Recognition in Arabic Text

hULMonA: The Universal Language Model in Arabic

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