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Journals / Journal of Soft Computing and Artificial Intelligence / 2023 / Cilt: 4 Sayı: 1

Classification of News Texts from Different Languages with Machine Learning Algorithms

Sidar AĞDUK
Emrah AYDEMİR
Ayfer POLAT
Pages
29–37
Publication Date
2023-06-25
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:As a result of the developments in technology, the internet is accepted as one of the most important sources of information today.
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Key Findings & Data:In this study, news texts obtained from online German, Spanish, English and Turkish news sites were separated according to predetermined world, sports, economy and politics categories.
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Conclusion & Impact:The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Text Mining · Text Classification · Machine Learning

Abstract

As a result of the developments in technology, the internet is accepted as one of the most important sources of information today. Although it is possible to access a large number of data in a short time thanks to the Internet, it is critical to analyze this data correctly. The need for text mining is increasing day by day by processing and analyzing the increasingly irregular text type data in the digital environment and classifying them in a meaningful way. In this study, news texts obtained from online German, Spanish, English and Turkish news sites were separated according to predetermined world, sports, economy and politics categories. The data set consisting of 4000 news texts was classified using 41 different machine learning algorithms in the Weka program. The highest successful classification was obtained with Naive Bayes Multinominal and Naive Bayes Multinominal Updateable algorithms, and 93.5% for German news texts, 93.3% for English news texts, 82.8% for Spanish news texts and 88.8% for Turkish news texts.

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Cite This Article

AĞDUK, S., AYDEMİR, E., POLAT, A. (2023). Classification of News Texts from Different Languages with Machine Learning Algorithms. Journal of Soft Computing and Artificial Intelligence, Cilt: 4 Sayı: 1, 29–37. https://ajindex.com/articles/952509

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