Impact of preprocessing on automatic text classification using supervised learning and reuters 21578
DOI:
https://doi.org/10.24054/rcta.v1i43.2506Keywords:
Automatic text classification, Preprocessing, Reuters 21578, machine learningAbstract
Faced with the increasing generation of digital data, challenges emerge in its management and categorization. This study emphasizes automatic text classification, placing special emphasis on the impact of preprocessing. By using the Reuters 21578 dataset and applying supervised learning algorithms such as Random Forest, k-Nearest Neighbors, and Naïve Bayes, we examined how techniques like tokenization and the removal of stop words influence classification accuracy. The findings underscore the added value of preprocessing, singling out "Random Forest" as the optimal algorithm, achieving a precision of 92.2%. This research illustrates the potential of combining preprocessing techniques and machine learning algorithms to enhance text categorization in the digital age.
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