Papers › Multi-level Product Category Prediction through Text Classification

Multi-level Product Category Prediction through Text Classification

3 Mar 2024arXiv:2403.01638archive 2025-07-28

Wesley Ferreira Maia, Angelo Carmignani, Gabriel Bortoli, Lucas Maretti, David Luz, Daniel Camilo Fuentes Guzman, Marcos Jardel Henriques, Francisco Louzada Neto

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 this specific task. The results showed that the BERT model, with an F1 Macro Score of up to 99% for segments, 96% for categories and subcategories and 93% for name products, outperformed LSTM in more detailed categories. However, LSTM also achieved high performance, especially after applying data augmentation and focal loss techniques. These results underscore the effectiveness of NLP techniques in retail and highlight the importance of the careful selection of modelling and preprocessing strategies. This work contributes significantly to the field of NLP in retail, providing valuable insights for future research and practical applications.

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Classification Data Augmentation Prediction Text Classification text-classification

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Focal Loss LSTM Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Sigmoid Activation Softmax Tanh Activation Weight Decay WordPiece

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