Multi-level Product Category Prediction through Text Classification
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.