A Comparative Survey on Arabic Stemming: Approaches and Challenges

Mustafa, Mohammad; Eldeen, Afag Salah; Bani‐Ahmad, Sulieman; Elfaki, Abdelrahman Osman

doi:10.4236/iim.2017.92003

Cited by 26 publications

(17 citation statements)

References 32 publications

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“…The same results were obtained by Al-Badarneh et al [27] and Mustafa et al [43], which demonstrated that stem is a better choice to use in classifying Arabic text. Our obtained result is consistent with the finding of Liu and Zhang [45] which showed that the pre-processing steps like stemming improves SA accuracy.…”

Section: Resultssupporting

confidence: 83%

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ASA: A framework for Arabic sentiment analysis

Oussous

Benjelloun

Lahcen

et al. 2019

Journal of Information Science

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Sentiment analysis (SA), also known as opinion mining, is a growing important research area. Generally, it helps to automatically determine if a text expresses a positive, negative or neutral sentiment. It enables to mine the huge increasing resources of shared opinions such as social networks, review sites and blogs. In fact, SA is used by many fields and for various languages such as English and Arabic. However, since Arabic is a highly inflectional and derivational language, it raises many challenges. In fact, SA of Arabic text should handle such complex morphology. To better handle these challenges, we decided to provide the research community and Arabic users with a new efficient framework for Arabic Sentiment Analysis (ASA). Our primary goal is to improve the performance of ASA by exploiting deep learning while varying the preprocessing techniques. For that, we implement and evaluate two deep learning models namely convolutional neural network (CNN) and long short-term memory (LSTM) models. The framework offers various preprocessing techniques for ASA (including stemming, normalisation, tokenization and stop words). As a result of this work, we first provide a new rich and publicly available Arabic corpus called Moroccan Sentiment Analysis Corpus (MSAC). Second, the proposed framework demonstrates improvement in ASA. In fact, the experimental results prove that deep learning models have a better performance for ASA than classical approaches (support vector machines, naive Bayes classifiers and maximum entropy). They also show the key role of morphological features in Arabic Natural Language Processing (NLP).

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Section: Resultssupporting

confidence: 83%

“…Stemming [43] is among the most important pre-processing steps in mining opinions as well as a very common requirement for natural language processing (NLP). Stemming standardises words by reducing each word to stem, base or root form.…”

Section: Our Approachmentioning

confidence: 99%

ASA: A framework for Arabic sentiment analysis

Oussous

Benjelloun

Lahcen

et al. 2019

Journal of Information Science

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“…The used datasets in these reported studies are very varied also and the majority of the employed test collections are not standard. In [3] the authors of this paper provide a complete survey for the developed algorithms of Arabic IR.…”

Section: Related Workmentioning

confidence: 99%

“…In contemporary time, the term Arabic has three forms [3] [4]: Classical Arabic (CA), Modern Standard Arabic (MSA) and Dialectal Arabic. Classical Arabic was the language of old Arabic-speaking people, e.g.…”

Section: Introductionmentioning

confidence: 99%

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Developing Two Different Novel Techniques for Arabic Text Stemming

Mustafa¹,

Aldeen²,

Zidan³

et al. 2019

IIM

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Stemming is used to produce stem or root of words. The process is vital to different research fields such as text mining, sentiment analysis, and text categorization, etc. Several techniques have been proposed to stemming Arabic text and among them, Khoja and light-10 stemmers are the most widely used. In this paper, we propose and evaluate two different stemming techniques to Arabic that are based on light stemming techniques. The new stemmers are compared to best reported light stemmer, which is light-10. Results and experiments, which were conducted using standard collections, reveal that The proposed stemmers yield 5.13% and 13.1% improvement in retrieval performance over light 10 with 0.369 average precision and 0.397, respectively and the improvement is statistically significant.

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