Classification of Text Documents based on Naive Bayes using N-Gram Features

• Published in: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) pp. 1 - 5
• Main Author: BAYGIN, Mehmet
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2018
• Subjects: Bayes methods; Data mining; document classification; Feature extraction; Machine learning; Naïve Bayes; Sentiment analysis; Sports; Training
• DOI: 10.1109/IDAP.2018.8620853

Document classification is basically the process of categorizing documents in certain categories correctly. This process, which is usually used in the field of text mining, automatically classifies documents with large dimensions. In this paper, Turkish document classification was performed by using Naïve Bayes approach which is one of the machine learning methods. With this approach, which basically uses 5 different categories, Turkish documents are classified quickly and automatically. In addition, the performance of the proposed approach was measured according to the basic evaluation criteria of precision, recall, accuracy and f-measure, and achieved a success rate of 92%. Also, the source codes of the application developed in this paper are presented as open source at https://drive.google.com/open?id=1Idp5VK1Q91vyqb940WjeoMpB9dVQuVC9.