applications (e.g., software tools, news classification)? Dialectal or Modern Standard Arabic? Let me know which direction you are interested in. (PDF) Arabic Topic Identification: A Decade Scoping Review

Arabic topic identification is a specialized field within Natural Language Processing (NLP) that involves classifying Arabic text into specific categories (e.g., politics, sports, culture). Given the language's unique morphological and syntactic structure, standard English-centric NLP techniques often underperform, requiring dedicated approaches to handle its complexity.

There is a significant gap between Modern Standard Arabic (MSA) used in formal writing and various spoken Arabic dialects (AD), requiring specialized models for each, especially since colloquial dialects are often used in social media datasets. Techniques for Arabic Topic Identification

Support Vector Machines (SVM) have proven superior for Arabic topic classification compared to others.

approaches (e.g., algorithms, BERT, datasets)?

Arabic dialects vary significantly across 22 countries, creating difficulties in developing universal models, often necessitating country-specific or dialectal classification methods.

Many contemporary Arabic texts are written without diacritics (vowels), causing the same word to be spelled in multiple ways, which creates challenges for automatic processing systems, including topic identification.