Classification of User Reports for Detection of Faulty Computer Components using NLP Models: A Case Study

Computer manufacturers typically offer platforms for users to report faults. However, there remains a significant gap in these platforms' ability to effectively utilize textual reports, which impedes users from describing their issues in their own words. In this context, Natural Language Processing (NLP) offers a promising solution, by enabling the analysis of user-generated text. This paper presents an innovative approach that employs NLP models to classify user reports for detecting faulty computer components, such as CPU, memory, motherboard, video card, and more. In this work, we build a dataset of 341 user reports obtained from many sources. Additionally, through extensive experimental evaluation, our approach achieved an accuracy of 79% with our dataset.

@article{silva2025_2503.16614,
  title={ Classification of User Reports for Detection of Faulty Computer Components using NLP Models: A Case Study },
  author={ Maria de Lourdes M. Silva and André L. C. Mendonça and Eduardo R. D. Neto and Iago C. Chaves and Felipe T. Brito and Victor A. E. Farias and Javam C. Machado },
  journal={arXiv preprint arXiv:2503.16614},
  year={ 2025 }
}