A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model

Habbat, Nassera; Anoun, Houda; Hassouni, Larbi

doi:10.15676/ijeei.2021.13.4.3

Cited by 17 publications

(5 citation statements)

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“…The selection of evaluation metrics can influence the manner in which the performance and efficacy of a model are compared and monitored. In our study, we used five different evaluation metrics, which are presented in Table 2 [1], [4].…”

Section: Performance Measuresmentioning

confidence: 99%

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Hicham

Karim

Habbat

2023

IJECE

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<span lang="EN-US">Arabic’s complex morphology, orthography, and dialects make sentiment analysis difficult. This activity makes it harder to extract text attributes from short conversations to evaluate tone. Analyzing and judging a person’s emotional state is complex. Due to these issues, interpreting sentiments accurately and identifying polarity may take much work. Sentiment analysis extracts subjective information from text. This research evaluates machine learning (ML) techniques for understanding Arabic emotions. Sentiment analysis (SA) uses a support vector machine (SVM), Adaboost classifier (AC), maximum entropy (ME), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), logistic regression (LR), and naive Bayes (NB). A model for the ensemble-based sentiment was developed. Ensemble classifiers (ECs) with 10-fold cross-validation out-performed other machine learning classifiers in accuracy (A), specificity (S), precision (P), F1 score (FS), and sensitivity (S).</span><p> </p>

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Section: Performance Measuresmentioning

confidence: 99%

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Hicham

Karim

Habbat

2023

IJECE

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“…It was trained on datasets from Arabic news websites: 1 billion tokens in 3.5 million articles from OSLAN Corpus and 1.5 billion words in 5 million articles from 10 primary news sources from 8 countries. Figure 1 shows the best-as-aservice technology, which activates layers without fine-tuning AraBERT settings [29]. It estimates the second-to-last concealed token pool's average.…”

Section: Arabertmentioning

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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“…In [8], Compared and evaluated various sentiment analysis models on Arabic tweets in the article. Performance of four deep learning models-CNN, LSTM, BI-LSTM, GRU, and a hybrid model (BI-LSTM + GRU) with three text representation techniques-was empirically evaluated (i.e., AraVec, FastText, AraBERT).…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques

Hameed

Abed²,

Sadiq

2023

Int. J. Interact. Mob. Technol.

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The subject of sentiment analysis through social media sites has witnessed significant development due to the increasing reliance of people on social media in advertising and marketing, especially after the Corona pandemic. There is no doubt that the prevalence of the Arabic language makes it considered one of the most important languages all over the world. Through human comments, it can know things if they are positive or negative. But in fact, the comments are many, and it takes work to evaluate the place or the product through a detailed reading of each comment. Therefore, this study applied deep learning approaches to this issue to provide final results that could be utilized to differentiate between the comments in the dataset. Arabic Sentiment Analysis was used and gave a percentage for each positive and negative commentary. This work used eight methods of deep learning techniques after using Fast Text as embedding, except Ara BERT. These techniques are the transformer (AraBERT), RNN (Long short-term memory (LSTM), Bidirectional long-short term memory (BI-LSTM), Gated recurrent units (GRUs), Bidirectional Gated recurrent units (BI-GRU)), CNN (like ALEXNET, proposed CNN), and ensemble model (CNN with BI-GRU). The Hotel Arabic Reviews Dataset was utilized to test the models. This paper obtained the following results. In the Ara BERT model, the accuracy is 96.442%. In CNN, like the Alex Net model, the accuracy is 93.78%. In the suggested CNN model, the accuracy is 94.43%. In the suggested LSTM model, the accuracy is 95%. In the suggested BI-LSTM model, the accuracy is 95.11%. The accuracy of the suggested GRU model is 95.07%. The accuracy of the suggested BI-GRU model is 95.02%. The accuracy is 94.52% in the Ensemble CNN with BI-GRU model that has been proposed. Consequently, the AraBERT outperformed the other approaches in terms of accuracy. Because the AraBERT has already been trained on some Arabic Wikipedia entries. The LSTM, BI-LSTM, GRU, and BI-GRU, on the other hand, had comparable outcomes.

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A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model

Cited by 17 publications

References 0 publications

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

Customer sentiment analysis for Arabic social media using a novel ensemble machine learning approach

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

Evaluation of Hotel Performance with Sentiment Analysis by Deep Learning Techniques

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