Stemming algorithms are used in information retrieval systems, indexers, text mining, text classifiers etc., to extract stems or roots of different words, so that words derived from the same stem or root are grouped together. Many stemming algorithms were built in different natural languages. Khoja stemmer is one of the known and widely used Arabic stemmers. In this paper, we introduced a new light and heavy Arabic stemmer. This new stemmer is presented in this study and compared with two well-known Arabic stemmers. Results showed that accuracy of our stemmer is slightly better than the accuracy yielded by each one of those two well-known Arabic stemmers used for evaluation and comparison. Evaluation tests on our novel stemmer yield 75.03% accuracy, while the other two Arabic stemmers yield slightly lower accuracy. Ó 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Online customer reviews are an important assessment tool for businesses as they contain feedback that is valuable from the customer perspective. These reviews provide a significant basis on which potential customers can select the product that best meets their preferences. In online reviews, customers describe positive or negative experiences with a product or service or any part of it (i.e. features). Consumers frequently experience difficulty finding the desired product for comparison because of the massive number of online reviews. The automatic extraction of important product features is necessary to support customers in search of relevant product features. These features are the criteria that make it possible for customers to characterise different types of products. This article proposes a domain independent approach for identifying explicit opinionated features and attributes that are strongly related to a specific domain product using lexicographer files in WordNet. In our approach, N_gram analysis and the SentiStrength opinion lexicon have been employed to support the extraction of opinionated features. The empirical evaluation of the proposed system using online reviews of two popular datasets of supervised and unsupervised systems showed that our approach achieved competitive results for feature extraction from product reviews.
Product aspect ranking is critical for prioritizing the most important aspects of a specific product/service to assist probable customers in selecting suitable products that can realize their needs. However, given the voluminous customer reviews published on websites, customers are hindered from manually extracting and characterizing the specific aspects of searched products. A few multicriteria decision-making methods have been implemented to rank the most relevant product aspects. As weights greatly affect the ranking results of product aspects, this study used objective methods in finding the importance degree of a criteria set to overcome the limitations of subjective weighting. The growing popularity of online shopping has led to an exponential increase in the number of customer reviews available on various e-commerce websites. The sheer volume of these reviews makes it nearly impossible for customers to manually extract and analyze the specific aspects of the products they are interested in. This challenge highlights the need for automated techniques that can efficiently rank the product aspects based on their relevance and importance. Multicriteria decision-making techniques can address the issue of product aspect ranking. These techniques seek to offer a methodical strategy for assessing and contrasting various product attributes based on various criteria. The subjective nature of determining weights for each criterion raises serious issues because it might lead to bias and inconsistent ranking outcomes. The CRITIC–VIKOR method was adopted in the product aspect ranking process. The statistical findings based on a benchmark dataset using NDCG demonstrate the superior performance of the method of using objective weighting to reasonably acquire subjective weighting results. Also, the results show that the product aspects ranked by using CRITIC–VIKOR could be considered guidelines for probable customers to make a wise purchasing decision.
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