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Food Public Opinion Prevention and Control Model Based on Sentiment Analysis.

Leiyang Chen1,2, Xiangzhen Peng1,2, Liang Dong1,2

  • 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, Wuhan 430001, China.

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Summary
This summary is machine-generated.

This study presents a deep learning model to predict and manage food public opinion, enhancing consumer trust and food safety. The Food Public Opinion Prevention and Control (FPOPC) model offers regulatory agencies effective sentiment management tools.

Keywords:
SAEfood public opinionfood safetypersonalized recommendationssentiment prediction

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Area of Science:

  • Food science and technology
  • Computational social science
  • Artificial intelligence

Background:

  • Food public opinion significantly impacts food safety and consumer trust due to its rapid spread and negativity.
  • Existing methods lack comprehensive strategies for managing the dynamic nature of food public opinion.

Purpose of the Study:

  • To develop and validate a Food Public Opinion Prevention and Control (FPOPC) model using deep learning and recommendation algorithms.
  • To enhance food safety and consumer trust through proactive management of public sentiment.

Main Methods:

  • Developed a comprehensive FPOPC framework covering all stages of public opinion development.
  • Utilized a Stacked Autoencoder (SAE) for sentiment prediction in food news based on user comments.
  • Implemented a collaborative filtering recommendation mechanism and an enhanced Bloom filter with HDFS for rapid opinion management.

Main Results:

  • The FPOPC model accurately predicts and controls the development of food public opinion.
  • Sentiment analysis of food news was effectively quantified and utilized for personalized recommendations.
  • Experimental validation confirmed the model's efficacy in managing food public sentiment across the supply chain.

Conclusions:

  • The FPOPC model provides a robust solution for predicting and controlling food public opinion.
  • This approach offers valuable tools for regulatory agencies to safeguard food safety and consumer confidence.