Classifications of Arabic Customer Reviews Using Stemming and Deep Learning
Abstract
With the emergence of AI text-based tools and applications, the need to present and investigate text-processing tools has also been raised. NLP tools and techniques have developed rapidly for some languages, such as English. However, other languages, like Arabic, still need to present more methods and techniques to present more explanations. In this study, we present a model to classify customer reviews which are written in Arabic. The HARD dataset is used to be adopted as the dataset. Three Deep Learning classifiers are adopted (CNN, LSTM, RNN). In addition to that, three stemmers are used as text processing techniques (Khoja, Snowball, Tashaphyne). Furthermore, another three feature extraction methods were utilized (TF-IDF, N-gram, BoW). The results of the model presented several explanations. The best performance resulted from using (CNN+ Snowball+ N-Gram) with an accuracy of (%93.5). The results of the model stated that some classifiers are sensitive toward using different stemmers, also some accuracy performance can be affected if there are different feature extraction methods used. Either stemming of feature extraction has an impact on the accuracy performance. The model also proved that the dialectal language could cause some limitations since different dialects can give conflict meaning across different regions or countries. The outcomes of the study open the gate towards investigating other tools and methods to enrich Arabic natural language processing and contribute to developing new applications that support Arabic content.
Keywords
References
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