Aspect-Based Sentiment Analysis of Arabic Restaurants Customers' Reviews Using a Hybrid Approach

Abstract

In this study, an Aspect-based Sentiment Analysis (ABSA) model was developed to classify restaurants' reviews in the Arabic language based on four predefined aspects: price, cleanliness, food quality, and service. A hybrid approach that combines machine learning with domain-specific dictionaries and sentiment word lists was proposed for ABSA. More than 3,000 reviews were collected from a restaurant reviews website. The reviews were annotated using a crowdsourcing method. The annotated reviews were pre-processed, then the dictionaries and sentiment word lists were extracted from the dataset. Moreover, a filter-based feature selection approach using the Chi2 method was applied to reduce the number of representative features. Four aspect models were built using Support Vector Machine (SVM) and another four models were built using Naïve Bayes (NB) classifiers, one model for each aspect. The models were evaluated using Accuracy, Precision, Recall, and F-Measure. The results were promising, as the price aspect model achieved the highest results by applying the SVM classifier with Accuracy 84.47%, Precision 84.3%, Recall 84.5%, and F-Measure 84.3%.