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

  • Abstract
  • Keywords
  • 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

      [1] Liu, B. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.

      [2] R. Decker, M. Trusov, Estimating aggregate consumer preferences from online product reviews, SSRN Electron. J. 27(4), 2010, pp. 293-307.

      [3] Kamal, A., Abulaish, M., & Anwar, T. 2012. Mining Feature-Opinion Pairs and their Reliability Scores from Web Opinion Sources. In Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, ACM.

      [4] Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of theACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, EMNLP ’02, pages 79–86, Stroudsburg,PA, USA, 2002. Association for Computational Linguistics. doi: 10.3115/1118693.1118704.

      [5] Riloff, E. and Wiebe, J. 2003. Learning Extraction Patterns for Subjective Expressions. In Proceeding of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP-03), pp. 105-112.

      [6] Berezina, K., Bilgihan, A., Cobanoglu, C., Okumus, F., 2016. Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. J. Hospitality Market. Manag. 25 (1), 1–24.

      [7] RuijiFu , Jiang Guo , Bing Qin , WanxiangChe , Haifeng Wang , Ting Liu, Learning semantic hierarchies: a continuous vector space approach, IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), v.23 n.3, p.461-471, March 2015

      [8] Qu, Z., Zhang, H., Li, H., 2008. Determinants of online merchant rating: content analysis of consumer comments about Yahoo merchants. Decis. Support Syst. 46 (1), 440–449..

      [9] Prabowo, R., &Thelwall, M. 2009. Sentiment Analysis: A Combined Approach, Journal of Informetrics, 3(2), pp. 143–157. .

      [10] Deng, S., Sinha, A.P., Zhao, H., 2017. Adapting sentiment lexicons to domain-specific social media texts. Decis. Support Syst. 94, 65–76.




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

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