Sentiment Analysis for Arabic Reviews using Machine Learning Classification Algorithms

Sayed, Awny; Elgeldawi, Enas; Zaki, Alaa M.; Galal, Ahmed R.

doi:10.1109/itce48509.2020.9047822

Cited by 20 publications

(12 citation statements)

References 19 publications

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“…For the multi-class case, they implemented the recursive neural tensor networks (RNTN). A comprehensive comparative analysis of various machine learning classifiers is provided in [6], and a review of sentiment analysis in the Arabic Language is given in [25]. A rich study of Algerian newspaper comments is presented in [26], in which the authors created their own corpus and used Support Vector Machine and Naïve Bayes to classify Arabic comments into positive and negative sentiments.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Machine learning algorithms have gained popularity in such a way that almost every scientific field now uses them. They have become integrated into numerous scientific disciplines, such as networking [4], transportation [5], text analysis [6,7], and bioinformatics [8]. This broad range of machine learning disciplines is due to their promising results and predictive performance in solving classification problems.…”

Section: Introductionmentioning

confidence: 99%

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Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

et al. 2021

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Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.

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Section: Sentiment Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

et al. 2021

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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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“…The first one was manually annotated, where each tweet is supplemented with a set of features computed automatically. Sayed et al (2020) used nine supervised machine learning algorithms. Namely, Gradient Boosting, Logistic Regression, Ridge Classifier, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbor KNN, Multi-layer Perceptron (MLP) and Naive Bayes classifiers for Arabic Sentiment Analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis of Arabic Reviews for Saudi Hotels Using Unsupervised Machine Learning

Alosaimi¹,

Alharthi²,

Alghamdi³

et al. 2020

Journal of Computer Science

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Virtual worlds such as social networking sites, blogs and content communities are extremely becoming one of the most powerful sources for news, markets, industries etc. These virtual worlds can be used for many aspects, because they are rich platforms full of feedback, emotions, thoughts and reviews. The main objective of this paper is to cluster Arabic reviews of Saudi hotels for sentiment analysis into positive and negative clusters. We used web scraping to collect Arabic reviews associated only with Saudi hotels, from the tourism website TripAdvisor and obtained in total 4604 Arabic reviews. Then the TF-IDF was applied to extract relevant features. An unsupervised learning approach was applied, in particular Kmeans and Hierarchical algorithms with two distance metrics: Cosine and Euclidean. Our manual labelled test data shows that the K-means algorithm with cosine distance performed well when applying all of our prepossessing steps. We concluded that the suggested prepossessing steps play a critical role in Arabic language processing and sentiment analysis.

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Sentiment Analysis for Arabic Reviews using Machine Learning Classification Algorithms

Cited by 20 publications

References 19 publications

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

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

Sentiment Analysis of Arabic Reviews for Saudi Hotels Using Unsupervised Machine Learning

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