Sentiment Classifier: Logistic Regression for Arabic Services’ Reviews in Lebanon

Omari, Marwan Al; Al-Hajj, Moustafa; Hammami, Nacereddine; Sabra, Amani

doi:10.1109/iccisci.2019.8716394

Cited by 28 publications

(18 citation statements)

References 10 publications

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“…Several implementations have considered the Arabic language for conducting SA using ML. For instance, Al Omari et al [35], logistic regression (LR) was applied on data extracted from reviews (including restaurant reviews) posted on Google and Zomato about public services in Lebanon. Several ML algorithms, namely, KNN, NB, SVM, LR, and RF, were applied for SA by Alharbi and Qamar [36] to assess customers' reviews about restaurants and cafes in the Qassim region of Saudi Arabia.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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Online media has an increasing presence on the restaurants' activities through social media websites, coinciding with an increase in customers' reviews of these restaurants. These reviews become the main source of information for both customers and decision-makers in this field. Any customer who is seeking such places will check their reviews first, which usually affect their final choice. In addition, customers' experiences can be enhanced by utilizing other customers' suggestions. Consequently, customers' reviews can influence the success of restaurant business since it is considered the final judgment of the overall quality of any restaurant. Thus, decision-makers need to analyze their customers' underlying sentiments in order to meet their expectations and improve the restaurants' services, in terms of food quality, ambiance, price range, and customer service. The number of reviews available for various products and services has dramatically increased these days and so has the need for automated methods to collect and analyze these reviews. Sentiment Analysis (SA) is a field of machine learning that helps analyze and predict the sentiments underlying these reviews. Usually, SA for customers' reviews face imbalanced datasets challenge, as the majority of these sentiments fall into supporters or resistors of the product or service. This work proposes a hybrid approach by combining the Support Vector Machine (SVM) algorithm with Particle Swarm Optimization (PSO) and different oversampling techniques to handle the imbalanced data problem. SVM is applied as a machine learning classification technique to predict the sentiments of reviews by optimizing the dataset, which contains different reviews of several restaurants in Jordan. Data were collected from Jeeran, a well-known social network for Arabic reviews. A PSO technique is used to optimize the weights of the features, as well as four different oversampling techniques, namely, the Synthetic Minority Oversampling Technique (SMOTE), SVM-SMOTE, Adaptive Synthetic Sampling (ADASYN) and borderline-SMOTE were examined to produce an optimized dataset and solve the imbalanced problem of the dataset. This study shows that the proposed PSO-SVM approach produces the best results compared to different classification techniques in terms of accuracy, F-measure, G-mean and AUC, for different versions of the datasets.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

et al. 2022

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“…The results of the MPAN model are shown in bold in the first column. This performance is expected, since the MPAN model produces state-of-the-art results on international public datasets such as those given in table VII [54] 92.1 BERT [9] 95.8 MT-DNN [55] 94.1 XLNet [56] 92.3 T5-11B [57] 92.0 IMDB = Movie Reviews Dataset; SNLI = Stanford Natural Language Inference Dataset; QQP = Quora Question Pairs; SciTail = Science Entailment; MultiNLI = Multiple Natural Language Inference [45] 95.98 88.10 94.24 AHS (sub) [45] 96.72 96.68 95.10 Ar-twitter [28] 94.47 85.01 84.20 88.10 ArSAS [46] 96.01 81.52 ASTD [30] 94.02 79.07 79.18 76.41 74.98 LABR [44] 96.43 89.60 LARGE Attraction Reviews [29] 98.65 96.20 LARGE Hotel Reviews [29] 97.83 91.70 LARGE Movie Reviews [29] 93.08 80.70 LARGE Products Reviews [29] 96.13 87.30 LARGE Restaurant Reviews [29] 96.07 78.50 OCLAR [47] 95.97 90.30 OD [35] 95.75 94.33 SMP(SYR) [42] 96.00 85.28 81.28 [58]. Alternatively, semi supervised MPAN models will also be explored, e.g., building on [59].…”

Section: B Performance Comparisons and Analysis For Asa Datasetsmentioning

confidence: 99%

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis

2021

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“…LR is an algorithm derived from a linear regression-based approach for predicting probability-dependent variables. This method performs well for binary classification tasks [29]. A logistic function or sigmoid function is used in this method to map a predicted value to a probabilistic number between 0 and 1.…”

Section: Classification Model Generationmentioning

confidence: 99%

Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning

Kusumaningrum

Nisa

Nawangsari

et al. 2021

Int. J. Adv. Intell. Informatics

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Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.

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Sentiment Classifier: Logistic Regression for Arabic Services’ Reviews in Lebanon

Cited by 28 publications

References 10 publications

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution

A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis

Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning

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