• Marwa O. Al Enany
• Amira M. Gaber

Text classification is crucial for categorizing news into several domains to alleviate the excessive information on social media, facilitate category identification, and improve data display to optimize the search process. Neutrosophic logic is an extension of fuzzy logic capable of addressing ambiguity, indeterminacy, and inconsistency in data. Neutrosophic text classification represents a viable methodology for classifying Arabic text. This work employs Neutrosophic sets for the classification of Arabic literature across several disciplines, including politics, education, health, and sports. The classification uses the Neutrosophic with SMAD dataset, an Arabic Social Media Dataset intended for text classification in social media with Arabic Natural Language Processing (ANLP) techniques. The Neutrosophic SMAD dataset underwent assessment by a 10-fold cross-validation method producing an average accuracy of 98.4%.

Neutrosophic Sets, Natural Language Processing (NLP), Arabic Natural Language Processing (ANLP), Fake News, Social Media, Arabic Corpus

"Combining Neutrosophics with SMAD Dataset for Arabic Text Classification of Social Media News Sources", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.e699-e706, December-2024, Available :http://www.jetir.org/papers/JETIR2412478.pdf

"Combining Neutrosophics with SMAD Dataset for Arabic Text Classification of Social Media News Sources", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppe699-e706, December-2024, Available at : http://www.jetir.org/papers/JETIR2412478.pdf