OCLAR: logistic regression optimisation for Arabic customers' reviews

Omari, Marwan Al

doi:10.1504/ijbidm.2022.122177

Cited by 4 publications

(4 citation statements)

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“…As dictionary-based methods require knowledge-based resources, languages with low dictionary resources rely on machine learning-based methods in sentiment analysis. Logistic regression has been employed to classify sentiment in Arabic [5][6][7] and Indonesian [13], with encouraging results. SVM and Naive Bayes have also been employed as classifiers to predict sentiment polarity [3,4,12].…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning techniques can be used to classify sentiment, such as Naï ve Bayes [3], and Support Vector Machine (SVM) [4] to classify sentiment opinion. Logistic regression has also been used to classify customer reviews [5][6][7]. Previous studies have also explored the application of deep learning algorithms to perform sentiment analysis of reviews, including application usage reviews and reviews of specific locations [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Oversampling and Undersampling on Aspect-Based Sentiment Analysis of Indramayu Tourism Using Logistic Regression

Chamidah,

Widiyanto,

Seta

et al. 2024

RIA

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Oversampling and Undersampling on Aspect-Based Sentiment Analysis of Indramayu Tourism Using Logistic Regression

Chamidah,

Widiyanto,

Seta

et al. 2024

RIA

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“…Even though most ASA strategies are traditional machine learning (ML), [17] used Logistic Regression (LR) to classify customer reviews by utilizing TF-IDF; the ways of categorizing emotions in Arabic and dialects were very similar. [18] investigated the comments made on Saudi social media platforms using machine learning.…”

Section: Related Workmentioning

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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“…The majority of existing techniques for Arabic Sentiment Analysis (ASA) primarily focus on traditional machine learning methods. For instance, Omari [17] applied Logistic Regression (LR) to classify customer reviews, achieving the best results using the Term Frequency-Inverse Document Frequency (TF-IDF) representation. Similar methodologies have been employed in numerous Arabic and dialect sentiment classification studies.…”

Section: Related Workmentioning

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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OCLAR: logistic regression optimisation for Arabic customers' reviews

Cited by 4 publications

References 0 publications

The Impact of Oversampling and Undersampling on Aspect-Based Sentiment Analysis of Indramayu Tourism Using Logistic Regression

The Impact of Oversampling and Undersampling on Aspect-Based Sentiment Analysis of Indramayu Tourism Using Logistic Regression

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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