Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Alwayle, Ibrahim M.; Al-onazi, Badriyya B.; Alalayah, Khaled M.; Alaidarous, Khadija M.; Ahmed, Ibrahim Abdulrab; Othman, Mahmoud M.; Motwakel, Abdelwahed

doi:10.32604/csse.2023.033834

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…This KNN obtained an accuracy rate of 64.65% for all emotions. In [21], a recognition and classification model, named TLBOML-ERC, was suggested to detect emotional expressions and sentiments expressed in Arabic tweets. This study employed a denoising autoencoder to classify emotional expressions in an Arabic tweet dataset into four emotion classes, "anger", "sadness", "joy", and "fear".…”

Section: Introductionmentioning

confidence: 99%

Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition

Ismaiel,

Alhalangy,

Mohamed

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Today, automatic emotion recognition in speech is one of the most important areas of research in signal processing. Identifying emotional content in Arabic speech is regarded as a very challenging and intricate task due to several obstacles, such as the wide range of cultures and dialects, the influence of cultural factors on emotional expression, and the scarcity of available datasets. This study used a variety of artificial intelligence models, including Xgboost, Adaboost, KNN, DT, and SOM, and a deep-learning model named SERDNN. ANAD was employed as a training dataset, which contains three emotions, "angry", "happy", and "surprised", with 844 features. This study aimed to present a more efficient and accurate technique for recognizing emotions in Arabic speech. Precision, accuracy, recall, and F1-score metrics were utilized to evaluate the effectiveness of the proposed techniques. The results showed that the Xgboost, SOM, and KNN classifiers achieved superior performance in recognizing emotions in Arabic speech. The SERDNN deep learning model outperformed the other techniques, achieving the highest accuracy of 97.40% with a loss rate of 0.1457. Therefore, it can be relied upon and deployed to recognize emotions in Arabic speech.

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Section: Introductionmentioning

confidence: 99%

Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition

Ismaiel,

Alhalangy,

Mohamed

et al. 2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

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Chimp Optimization Algorithm with Deep Learning-Driven Fine-grained Emotion Recognition in Arabic Corpus

bin Subait,

Basheti,

Al Sultan

et al. 2024

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Recently, emotion analysis and classification of tweets have become a crucial area of research. The Arabic language had experienced difficulties with emotion classification on Twitter(X), needing preprocessing more than other languages. Emotion detection is a major challenge in Natural Language Processing (NLP), which allows machines to ascertain the emotions expressed in the text. The task includes recognizing and identifying human feelings such as fear, anger, sadness, and joy. The discovered sentiments and feelings expressed in tweets have gained much recognition in recent years. The Arab region has played a substantial role in international politics and the global economy needs to scrutinize the emotions and sentiments in the Arabic language. Lexicon-based and machine-learning techniques are two common models that address the problems of emotion classification. This study introduces a Chimp Optimization Algorithm with a Deep Learning-Driven Arabic Fine-grained Emotion Recognition (COADL-AFER) technique. The presented COADL-AFER technique mainly aims to detect several emotions in Arabic tweets. In addition to its academic significance, the COADL-AFER technique has practical applications in various fields, including enhancing applications of E-learning, aiding psychologists in recognising terrorist performance, improving product quality, and enhancing customer service. The COADL-AFER technique applies the long short-term memory (LSTM) model for emotion detection. Finally, the hyperparameter selection of the LSTM method can be accomplished by COA. The experimental validation of the COADL-AFER system, a crucial step in our research, is verified utilizing the Arabic tweets dataset. The simulation results stated the betterment of the COADL-AFER technique, further reinforcing the reliability of our research.

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Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Cited by 2 publications

References 23 publications

Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition

Deep Learning, Ensemble and Supervised Machine Learning for Arabic Speech Emotion Recognition

Chimp Optimization Algorithm with Deep Learning-Driven Fine-grained Emotion Recognition in Arabic Corpus

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