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"Commerce Dictionaries Arabic"
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Deep Learning Analysis for Reviews in Arabic E-Commerce Sites to Detect Consumer Behavior towards Sustainability
Recently, online e-commerce has developed a major method for customers to buy various merchandise. Deep learning analysis of online customer reviews can detect consumer behavior towards sustainability. Artificial intelligence can obtain insights from product reviews to design sustainable products. A key challenge is that many sustainable products do not seem to fulfill consumers’ expectations due to the gap between consumers’ expectations and their knowledge of sustainable products. This article proposes a new deep learning model using dataset analysis and a neural computing dual attention model (DL-DA). The DL-DA model employs lexical analysis and deep learning methodology. The lexical analysis can detect lexical features in the customer reviews that emphasize sustainability. Then, the deep learning model extracts the main lexical and context features from the customer reviews. The deep learning model can predict customers’ repurchase habits concerning products that favor sustainability. This research collected data by crawling Arabic e-commerce websites for training and testing. The size of the collected dataset is about 323,150 customer reviews. The experimental results depict that the proposed model can efficiently enhance the accuracy of text lexical analysis. The proposed model achieves accuracy of 96.5% with an F1-score of 96.1%. We also compared the proposed model with state of the art models, where our model enhances both accuracy and sensitivity metrics by more than 5%.
Journal Article
Arabic Aspect-Based Sentiment Classification Using Seq2Seq Dialect Normalization and Transformers
Sentiment analysis is one of the most important fields of natural language processing due to its wide range of applications and the benefits associated with using it. It is defined as identifying the sentiment polarity of natural language text. Researchers have recently focused their attention on Arabic SA due to the massive amounts of user-generated content on social media and e-commerce websites in the Arabic world. Most of the research in this fieldwork is on the sentence and document levels. This study tackles the aspect-level sentiment analysis for the Arabic language, which is a less studied version of SA. Because Arabic NLP is challenging and there are few available Arabic resources and many Arabic dialects, limited studies have attempted to detect aspect-based sentiment analyses on Arabic texts. Specifically, this study considers two ABSA tasks: aspect term polarity and aspect category polarity, using the text normalization of the Arabic dialect after making the classification task. We present a Seq2Seq model for dialect normalization that can serve as a pre-processing step for the ABSA classification task by reducing the number of OOV words. Thus, the model’s accuracy increased. The results of the conducted experiments show that our models outperformed the existing models in the literature on both tasks and datasets.
Journal Article
Arabic Stemmer Based Big Data
By its morphological and syntactic richness, the Arabic language is considered among the most difficult languages to deal with it in the field of information search. This is due; in particular, to the various difficulties encountered in its Stemming, which has not yet experienced a standard approach. The Stemming algorithm for Arabic words has been an important topic in Arabic information retrieval. The intention of this article is to parallelize a stemming algorithm for Arabic by proposing a distributed stemming algorithm in a big data system. This is by using the Hadoop framework, the MapReduce programming model for the development of the algorithm, and the distributed file system HDFS for the Storage of stemming result.
Journal Article