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Multi-level Product Category Prediction through Text Classification
March 5, 2024, 2:52 p.m. | Wesley Ferreira Maia, Angelo Carmignani, Gabriel Bortoli, Lucas Maretti, David Luz, Daniel Camilo Fuentes Guzman, Marcos Jardel Henriques, Francisco L
cs.CL updates on arXiv.org arxiv.org
Abstract: This article investigates applying advanced machine learning models, specifically LSTM and BERT, for text classification to predict multiple categories in the retail sector. The study demonstrates how applying data augmentation techniques and the focal loss function can significantly enhance accuracy in classifying products into multiple categories using a robust Brazilian retail dataset. The LSTM model, enriched with Brazilian word embedding, and BERT, known for its effectiveness in understanding complex contexts, were adapted and optimized for …
abstract accuracy advanced article arxiv augmentation bert classification cs.cl data function loss lstm machine machine learning machine learning models multiple prediction product products retail sector study text text classification through type
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