Extraction of Food Hazards using Online Food Review (OFR) Sentiment Mining on Social Networks

 
 
 
  • Abstract
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  • References
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  • Abstract


    Advanced data examination is a standout amongst the most progressive innovative improvements in the present yearthat empowers the disclosure of highlighting patterns through complex mathematical strategies. In different social stages, a great many food reviews are distributed by clients, which can possibly furnish producers with priceless experiences into food quality. This paper introduces an outline structure to dissect online food reviews. The goal is to utilize this human-produced data to distinguish a progression of client needs. The structure intends to distil substantial number of subjective data into quantitative bits of knowledge on item includes, with the goal that originators can settle on more educated choices. The system joins the components of online food reviews, outline hypothesis and procedure, and data examination to uncover new bits of knowledge. The viability of the proposed structure is approved through a contextual investigation of food reviews from the social sites. The structure is described by an incorporation of key characteristic language preparing methods and machine learning calculations with Naive Bayes algorithm. Above all, an organized computational process, known as the Machine Model, is endorsed to naturally perform opinion investigation on given Online Food Review (OFRs).

     


  • Keywords


    Natural Language Processing (NLP), Online Food Review (OFR), machine model, Client Needs.

  • References


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Article ID: 22069
 
DOI: 10.14419/ijet.v7i4.19.22069




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