Comparative Study of Arabic Stemming Algorithms for Topic Identification

Naili, Marwa; Chaïbi, Anja Habacha; Ghezala, Henda Ben

doi:10.1016/j.procs.2019.09.238

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…However, three recent comparative studies investigated different Arabic stemmers in the field of Arabic text classification. The first one [21] reported that the classification was more efficient with Tashaphyne light based stemmer 6 followed by Farasa [22], Khoja stemmer [8], Light10 [7], and finally Al Khalil Morph Sys [23]. What's more, this study found that the stemmer accuracy does not have an impact on the classifier efficiency in topic identification.…”

Section: Stemmingmentioning

confidence: 64%

The effects of Pre-Processing Techniques on Arabic Text Classification

2021

IJATCSE

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In the last two decades, the amount of available Arabic text data on the World Wide Web is dramatically growing, making it the fourth most used language on the web. Accordingly, the demand for efficient Arabic text classification is increasing, especially for web page content filtering, information retrieval, and e-mail spam detection. Several Machine Learning algorithms have been implemented to classify Arabic documents. However, the results achieved are not comparable with those obtained in other languages such as English, primarily when using preprocessing techniques that do not take into consideration the Arabic language features. This paper investigates the impact of wisely selected preprocessing techniques on the efficiency of different text classification algorithms. The effects of stop words removal, stemming, lemmatization, and all possible combinations are examined. The reported results (+10.75% to +28.73%) prove the effectiveness of using these techniques either individually or in combination.

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Section: Stemmingmentioning

confidence: 64%

The effects of Pre-Processing Techniques on Arabic Text Classification

2021

IJATCSE

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“…The stemming process is an important pre-processing phase that, depending on the language employed, might be considered a tough step to complete. The amount of morphological complexity of a language can impact stemming outcomes [25]. Because the NLTK library [26], which is used for the stemming process, does not currently support Indonesian, the stemming process in Indonesian is carried out using the Sastrawi library [27], which has proved to be fairly competent in handling the Indonesian language stemming process.…”

Section: Data Pre-processingmentioning

confidence: 99%

Analysis of named-entity effect on text classification of traffic accident data using machine learning

Putra

Girsang

2022

IJEECS

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<span lang="EN-US">With the rising number of accidents in Indonesia, it is still necessary to evaluate and analyze accident data. The categorization of traffic accident data has been developed using word embedding, however additional work is needed to achieve better results. Several informative named entities are frequently sufficient to differentiate whether or not information on a traffic accident exists. Named-entities are informational characteristics that can offer details about a text. The influence of named-entities on thematic text categorization is examined in this paper. The information was collected using a Twitter social media crawl. Preprocessing is done at the beginning of the process to modify and delete useful text as well as label specified entities. On Support Vector Machine (SVM), scheme comparisons were performed for (i) Word Embedding, (ii) the number of occurrences of Named Entities, and (iii) the combination of the two is known as a Hybrid. The Hybrid scheme produced an improvement in classification accuracy of 90.27 percent when compared to Word Embedding scheme and occurrences of named entities scheme, according to tests conducted using 1.885 data consisting of 788 accident data and 1.067 non-accident data.</span>

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“…According to our review, the commonly stemmers that have been successfully implemented in Arabic topic identification [26][27][28] are ARLSTem, Tashaphyne light stemmer, Farasa, Khoja stemmer, Light10, Al Khalil Morph Sys, Assem's stemmer, Soori's stemmer, and ISRI stemmer.…”

Section: Stemmingmentioning

confidence: 99%

Arabic Topic Identification: A Decade Scoping Review

Kah

Zeroual

2021

E3S Web Conf.

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Arabic topic identification is a part of text classification that aims to assign a given text a set of pre-defined classes (i.e., topics) based on its content and extracted features. This task can be performed using rule-based methods or data-driven approaches. These latter gained more popularity since they require much less human effort to accurately classify a large number of documents. Due to the tremendous growth of Web contents primarily in news websites and social media, topic identification had received a great deal of attention over the last years, and has become a cornerstone of both search engines and information retrieval. The Arabic language is the fourth most used language on the web and records the highest growth in the last two decades (2000–2020). Based on these facts currently available, it seems fair to look closer at the advancements in the Arabic topic identification in the last decade. To this end, we performed the first of its kind scoping review that addresses recent studies in the field of Arabic topic identification that follows the PRISMA-ScR guidelines. This review is based on various online bibliographic databases (e.g., Springer, ScienceDirect, and IEEE Xplore) and datasets search engines (e.g., Google Dataset Search).

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Comparative Study of Arabic Stemming Algorithms for Topic Identification

Cited by 13 publications

References 3 publications

The effects of Pre-Processing Techniques on Arabic Text Classification

The effects of Pre-Processing Techniques on Arabic Text Classification

Analysis of named-entity effect on text classification of traffic accident data using machine learning

Arabic Topic Identification: A Decade Scoping Review

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