A two-level deep learning approach for emotion recognition in Arabic news headlines

Elsayed, Hoda Ahmed Galal; Chaffar, Soumaya; Belhaouari, Samir Brahim; Raissouli, Hafsa

doi:10.1080/1206212x.2020.1851501

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Apart from this, a number of other ways and strategies have been tested in a variety of settings. For example, the wavelet transform (WT) has been widely used to extract features [18]. ese techniques extract properties from signals based on their frequency of occurrence.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

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In recent years, COVID-19 has been regarded as the most dangerous pandemic for several countries. On various social media platforms, such as Twitter, Facebook, and Instagram, a variety of rumours, hypes, and news are published. This might have a detrimental impact on people’s life. As a result, social media platforms have always had a difficult time authenticating this fake information. Different machine learning (ML) and deep learning (DL) classifiers were used in this work to categorize the continuing impacts of tweets and forecast their after-effects. Support vector machine (SVM), random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) were used for classification, while AdaBoost and convolutional neural network (CNN) were utilized for future effects. The tweets dataset from Kaggle was used to train the SVM, RF, KNN, and DT models, which were then assessed on multiple evaluation criteria such as accuracy, precision, recall, and F1-score, using a 70 : 30 ratio. The CNN and AdaBoost, on the other hand, have been taught to detect the mean square error, root mean square error, and mean absolute error. With 0.74 and 0.73 percent score out of 1, respectively, RF and SVM exhibit the best accuracy in impact when classifying the outcomes on the obtained dataset. In terms of a regression problem, CNN beat the ADA Regressor across the board.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Zaidi

Chatterjee

Belhaouari

2022

Applied Computational Intelligence and Soft Computing

Self Cite

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“…On the other hand, this training-based ensemble needs to be more accurate in situations with inadequate data. The EL process is contingent on how predictions and base learners are coupled, more specifically, on whether rule-based or meta-learning approaches are applied and whether the learning process is concurrent or sequential [11], [12]. The difference between heterogeneous and homogeneous ensembles is that heterogeneous ensembles contain many classifiers, while homogeneous ensembles contain multiple instances of the same model.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Arabic E-Commerce Review Sentiment Analysis Using a hybrid Deep Learning Model and FastText word embedding

Hicham,

Nassera,

Karim

2023

EAI Endorsed Trans IoT

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The usage of NLP is shown in sentiment analysis (SA). SA extracts textual views. Arabic SA is challenging because of ambiguity, dialects, morphological variation, and the need for more resources available. The application of convolutional neural networks to Arabic SA has shown to be successful. Hybrid models improve single deep learning models. By layering many deep learning ensembles, earlier deep learning models should achieve higher accuracy. This research successfully predicted Arabic sentiment using CNN, LSTM, GRU, BiGRU, BiLSTM, CNN-BiGRU, CNN-GRU, CNN-LSTM, and CNN-biLSTM. Two enormous datasets, including the HARD and BRAD datasets, are used to evaluate the effectiveness of the proposed model. The findings demonstrated that the provided model could interpret the feelings conveyed in Arabic. The proposed procedure kicks off with the extraction of Arabert model features. After that, we developed and trained nine deep-learning models, including CNN, LSTM, GRU, BiGRU, BiLSTM, CNN-BiGRU, CNN-GRU, CNN-LSTM, and CNN-biLSTM. Concatenating the FastText and GLOVE as word embedding models. By a margin of 0.9112, our technique surpassed both standard forms of deep learning.

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“…Ensemble modeling has become an increasingly popular method for enhancing the overall performance of NLP models. However, this training-based ensemble tends to overfit in situations with limited data [11,12].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Arabic Sentiment Analysis in E-Commerce Reviews on Social Media Through a Stacked Ensemble Deep Learning Approach

Hicham¹,

Karim²,

Habbat³

2023

MMEP

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Sentiment analysis (SA) employs natural language processing techniques to extract opinions from textual data. Applying SA to the Arabic language presents numerous challenges, including ambiguity, the presence of multiple dialects, a need for additional resources, and morphological variation. The domain of Arabic SA has witnessed significant advancements with the application of deep learning (DL) approaches, such as convolutional neural networks (CNNs). The performance of single DL models has been further improved by hybrid models combining CNNs with bidirectional long short-term memory (Bi-LSTM) or bidirectional gated recurrent units (Bi-GRU). It is anticipated that the accuracy of these DL models can be enhanced through stacked deep learning ensembles. In this study, a stacked ensemble approach is proposed that accurately predicts Arabic sentiment by leveraging the predictive capabilities of CNN, Bi-GRU, Bi-LSTM, and hybrid DL models (CNN-Bi-GRU and CNN-Bi-LSTM). The proposed model's efficacy is evaluated using four extensive datasets: the HARD dataset, the BRAD dataset, the ARD dataset, and a real dataset composed of 71,583 Arabic reviews. Experimental results demonstrate the suitability of the proposed model for analyzing sentiments in Arabic texts. The method's first step involves feature extraction using the AraBERT model. Subsequently, five DL models are developed and trained, including CNN, Bi-GRU, Bi-LSTM, a hybrid CNN-Bi-GRU model, and a hybrid CNN-LSTM model. Finally, the outputs of the base classifiers are concatenated using the multilayer perceptron algorithm. Our approach achieves an improved accuracy of 0.9256 compared to basic and hybrid deep learning methods.

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A two-level deep learning approach for emotion recognition in Arabic news headlines

Cited by 5 publications

References 38 publications

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers

Enhancing Arabic E-Commerce Review Sentiment Analysis Using a hybrid Deep Learning Model and FastText word embedding

Enhancing Arabic Sentiment Analysis in E-Commerce Reviews on Social Media Through a Stacked Ensemble Deep Learning Approach

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