Arabic.doi Instant

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

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. Arabic.doi

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

Techniques like Term Frequency-Inverse Document Frequency (TFIDF) and k-Nearest Neighbors (kNN) are used, often combined with triggers (i.e., Average Mutual Information) to improve results. Essential steps include removing diacritics

Arabic has high derivational and inflectional complexity. For example, a single word can include affixes (prefixes, suffixes, infixes) that represent pronouns, conjunctions, and prepositions.

Essential steps include removing diacritics, normalization, tokenization, stop-word removal, and morphological analysis to extract roots or stems.

Arabic is derived from triconsonantal roots. Hundreds of distinct words can stem from a single root, making root-based stemming (finding the root) or lemmatization (finding the dictionary form) crucial for reducing vocabulary size and identifying topics.