mlsanityData to 14 Oct 2026

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

arXiv 2503.16614 ↗First posted 20 Mar 2025cs.CL cs.AI cs.LGcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

12 of 21 references.

References · 12

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Evaluating LLMs and Prompting Strategies for Automated Hardware Diagnosis from Textual User-Reportscited by 1