PyArabic: A Python package for Arabic text

Zerrouki, Taha

doi:10.21105/joss.04886

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Normalizing text is an important preprocessing step in natural language processing that involves transforming text data into a standardized format. Normalization of Arabic text involves several sub-tasks, including removing diacritics (Zerrouki, 2023), normalizing characters, and removing ligatures. These sub-tasks are essential for improving the accuracy of downstream tasks such as text classification, named entity recognition, and sentiment analysis.…”

Section: Statement Of Needmentioning

confidence: 99%

Tashaphyne: A Python package for Arabic Light Stemming

Zerrouki

2024

JOSS

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Stemming is an important task in natural language processing that involves reducing a word to its root form, or stem. In many cases, stemming can significantly improve the accuracy and efficiency of text analysis tasks such as information retrieval, text classification, and sentiment analysis. For the Arabic language, which has a rich morphology with a large number of prefixes and suffixes, stemming is particularly challenging. Tashaphyne provides an effective solution to this challenge, making it a valuable tool for researchers and practitioners working with Arabic text data.

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Section: Statement Of Needmentioning

confidence: 99%

Tashaphyne: A Python package for Arabic Light Stemming

Zerrouki

2024

JOSS

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“…• While some existing text-processing packages, e.g. Zerrouki (2022), focus on a specific group of languages, Arabica offers text-mining methods for all Latin Alphabet languages, including the stopwords removal of 18 lists of stopwords included in the NLTK corpus of stopwords.…”

Section: Statement Of Needmentioning

confidence: 99%

Arabica: A Python package for exploratory analysis of text data

Koráb,

Poměnková

2024

JOSS

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Research meta-data is typically recorded as a time series with dimensions of cross-sections (e.g., article title, journal, volume, issue, author's names, and affiliations) and time (e.g., publication date). Meta-datasets provide valuable insights into the research trends in a particular field of science. Meta-analysis (a group of methods to analyze research meta-data) currently does not implement text analytics in either programming language. This package aims to fill that need. Arabica offers descriptive analytics, visualization, sentiment classification, and structural break analysis for exploratory analysis of research meta-datasets in easy-to-use Python implementation.

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