Hybrid Feature Model for Emotion Recognition in Arabic Text

Nourah Alswaidan,Mohamed El Bachir Menai

doi:10.1109/access.2020.2975906

Nourah Alswaidan, Mohamed El Bachir Menai

Open Access

https://doi.org/10.1109/access.2020.2975906

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Abstract

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 macro-averaged 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.

Highlights

Because we rely on computers to perform our daily tasks, the need for improved human-computer interactions has increased
The performance results on the Iraqi Arabic emotion dataset (IAEDS) dataset show that the human-engineered feature-based (HEF) model achieved the highest performance results for happiness and surprise; the deep feature-based (DF) model achieved the highest performance results for disgust and fear; and the HEF+DF model achieved the highest performance results for anger and sadness
The DF model performed better than the HEF model on the SemEval-2018 dataset; the SemEval-2018 dataset was more imbalanced than the IAEDS dataset

Summary

INTRODUCTION

Because we rely on computers to perform our daily tasks, the need for improved human-computer interactions has increased. Motivated by the objective of boosting the research on Arabic NLP, this paper proposes three models, a humanengineered feature-based (HEF) model, a deep feature-based (DF) model, and a hybrid model (HEF+DF) for emotion recognition in Arabic text. Menai: Hybrid Feature Model for Emotion Recognition in Arabic Text layer using four different pre-trained word embedding models. It outperformed the state-of-the-art models on the AETD dataset in terms of accuracy, weighted-average precision (Pweighted ), weighted-average recall (Rweighted ), and weighted-average F-score (F weighted ) It achieved the best F macro and the second-best Jaccard accuracy and micro-averaged F-score (F micro) on the SemEval-2018 dataset.

RELATED WORK

EXPERIMENTS

EVALUATION MEASURES

PERFORMANCE RESULTS

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CONCLUSION