Abstract
AbstractText classification is the process of determining categories or tags of a document depending on its content. Although text classification is a well‐known process, it has many steps that require tuning to improve mathematical models. This article provides a novel methodology and expresses key points to improve text classification performance using learning‐based algorithms and techniques. First, to check the effectiveness of the proposed methodology, we selected two public Turkish news benchmarking datasets. Then, we performed extensive testing using both supervised machine learning algorithms and state‐of‐art pre‐trained language models. The experimental results show that our methodology outperforms previous news classification studies on these benchmarking datasets improving categorization results based on F1‐score. Therefore, we conclude that the presented methodology efficiently improves the classification results and selects the feasible classifier for a given dataset.
Published Version
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