A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection

Abo, Mohamed Elhag Mohamed; Idris, Norisma; Mahmud, Rohana; Qazi, Atika; Hashem, Mohamed; Maitama, Jaafar Zubairu; Naseem, Usman; Khan, Shah Khalid; Yang, Shuiqing

doi:10.3390/su131810018

Cited by 30 publications

(18 citation statements)

References 77 publications

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“…The result showed that Bidirectional Multi-Layer LSTM has high accuracy. In [ 12 ], author applied SVM, LR, DT, NB, and DL models on the Saudi dialect sentiment Arabic tweets dataset. The result shows that deep learning and SVM classifiers perform best with accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, the research used ML algorithms to predict sentiment analysis and to enhance the sentiment analysis method’s accuracy [ 9 , 10 , 11 ], transit from primarily linear models and evolved until it reached what we see today of the more complex deep neural network. In addition, deep learning (DL) algorithms have been used to extract features with great effectiveness over other ML algorithms such as [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis

Saleh

Mostafa

Alharbi

et al. 2022

Sensors

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Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people’s opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content’s sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model’s performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis

Saleh

Mostafa

Alharbi

et al. 2022

Sensors

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“…The performance of each classifier was measured by computing the following performance measures: classification accuracy, precision, recall, and F1-score. These measures are commonly used to evaluate the performance of ML models in many research areas, such as rumor detection systems [43,44], clickbait detection [33], as well as in SA [45][46][47].…”

Section: Evaluation Metricsmentioning

confidence: 99%

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

et al. 2022

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Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users’ opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services’ mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features.

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“…a) Fifth phase: Hybrid supervised classification approach phase: This phase performs two subsections: the first issue is applying ML approach which performs five selected ML classifiers which utilized extensively for ASA: Logistic Regression (LR) [25] [26] [27] [28], Naïve Bayes (NB) [29] [30] [31] [32], K-Nearest Neighbors (KNN) [33] [34] [31] [35], Random Forest (RF) [36] [37] [38] and SVM [33] [39] [40] in addition applying DL approach which performs DL classifier Multi-Layer Perceptron Neural Network (MLP-NN) which applied in [36] [37] for ASA.…”

Section: ) Stop Word Removalmentioning

confidence: 99%

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

Saudy¹,

El-Ghazaly²,

Nasr³

et al. 2022

IJACSA

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Arabic language incurs from the shortage of accessible huge datasets for Sentiment Analysis (SA), Machine Learning (ML), and Deep Learning (DL) applications. In this paper, we present MASR, a simple Mobile Applications Arabic Slang Reviews dataset for SA, ML, and DL applications which comprises of 2469 Egyptian Mobile Apps reviews, and help app developers meet user requirements evolution. Our methodology consists of six phases. We collect mobile apps reviews dataset, then apply preprocessing steps, in addition perform SA tasks. To evaluate MASR datasets, first we apply ML classification techniques: K-Nearest Neighbors (K-NN), Support vector machine (SVM), Logistic Regression (LR), and Random Forest (RF), and DL classification technique: Multi-layer Perceptron Neural Network (MLP-NN). From the examination for pervious classification techniques, we adopted a hybrid classification approach combined from the top two ML classifier accuracy results (LR, RF), and DL classifier (MLP-NN). The findings prove the adequacy of a hybrid supervised classification approach for MASR datasets.

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A Multi-Criteria Approach for Arabic Dialect Sentiment Analysis for Online Reviews: Exploiting Optimal Machine Learning Algorithm Selection

Cited by 30 publications

References 77 publications

Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis

Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis

An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique

A Novel Hybrid Sentiment Analysis Classification Approach for Mobile Applications Arabic Slang Reviews

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