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This research presents an innovative NLP-based intent detection algorithm designed for smart house control via WhatsApp messages. The proposed method leverages a Bi-LSTM classifier to accurately identify user intents expressed in natural language. Experimental results demonstrate the model’s effectiveness, achieving a training accuracy of 98.7% in the validation phase and a loss function approaching 0.0032, indicating a robust and production-ready solution for intelligent home automation applications.

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NLP Combined with Bi-LSTM Classifier Based Intent Detection Algorithm for Smart House Control Using WhatsApp Message

  • 会议论文

  • 发表日期：

    2026年

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  • Smail Tigani,
  • Hassna Chaibi,
  • Rachid Saadane,
  • Abdellah Chehri

摘要

This research presents an innovative NLP-based intent detection algorithm designed for smart house control via WhatsApp messages. The proposed method leverages a Bi-LSTM classifier to accurately identify user intents expressed in natural language. Experimental results demonstrate the model’s effectiveness, achieving a training accuracy of 98.7% in the validation phase and a loss function approaching 0.0032, indicating a robust and production-ready solution for intelligent home automation applications.