Trust Analysis for Information Concerning Food-Related Risks

  • Alessandra AmatoEmail author
  • Giovanni Cozzolino
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


In last years many business activities, scientific researches and applications exploit social networks as important sources for gathering data with different aims. Knowing the habits and preferences of user can be useful for different purposes, firstly to build marketing and advertising campaigns, but also to analyse other social phenomena for statistics, demography or security reasons. Thanks to their wide adoption among people, social networks are becoming the first media adopted to publish and share real-time news about happening events and, consequently, also the main media to retrieve information on what happens around you. Taking into account this consideration, in this paper we investigate a methodology for semantic analysis of textual information obtained from social media streams, in order to perform an early identification of food contaminations. As a case study, we consider a set of reviews gathered from the social network Yelp [26], on which we perform the textual analysis foreseen in the proposed methodology.



This work is supported by CREA European Project: Conflict Resolution with Equitative Algorithms, Grant Agreement number: 766463, CREA, JUST-AG-2016/JUST-AG-2016-05.


  1. 1.
    Albanese, M., D’acierno, A., Moscato, V., Persia, F., Picariello, A.: Modeling recommendation as a social choice problem. In: Proceedings of the 4th ACM Conference on Recommender Systems, RecSys 2010, pp. 329–332 (2010)Google Scholar
  2. 2.
    Amato, F., Cozzolino, G., Maisto, A., Mazzeo, A., Moscato, V., Pelosi, S., Picariello, A., Romano, S., Sansone, C.: ABC: a knowledge based collaborative framework for E-health, pp. 258–263 (2015)Google Scholar
  3. 3.
    Amato, F., Cozzolino, G., Mazzeo, A., Pizzata, A.: Sentiment analysis on Yelp social network, pp. 92–98 (2017)Google Scholar
  4. 4.
    Amato, F., Cozzolino, G., Moscato, V., Picariello, A., Sperlí, G.: Automatic personalization of visiting path based on users behaviour, pp. 692–697 (2017)Google Scholar
  5. 5.
    Balzano, W., Murano, A., Stranieri, S.: Logic-based clustering approach for management and improvement of VANETs. J. High Speed Netw. 23(3), 225–236 (2017)CrossRefGoogle Scholar
  6. 6.
    Cilardo, A.: Exploring the potential of threshold logic for cryptography-related operations. IEEE Trans. Comput. 60(4), 452–462 (2011)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Cilardo, A., Durante, P., Lofiego, C., Mazzeo, A.: Early prediction of hardware complexity in HLL-to-HDL translation. In: International Conference on Field Programmable Logic and Applications (FPL), pp. 483–488. IEEE (2010)Google Scholar
  8. 8.
    Cozzolino, G.: Using semantic tools to represent data extracted from mobile devices, pp. 530–536 (2018)Google Scholar
  9. 9.
    D’Acierno, A., Moscato, V., Persia, F., Picariello, A., Penta, A.: iWIN: a summarizer system based on a semantic analysis of web documents. In: Proceedings of the IEEE 6th International Conference on Semantic Computing, ICSC 2012, pp. 162–169 (2012)Google Scholar
  10. 10.
    Doan, S., Bastarache, L., Klimkowski, S., Denny, J.C., Xu, H.: Integrating existing natural language processing tools for medication extraction from discharge summaries. J. Am. Med. Inform. Assoc. 17(5), 528–531 (2010)CrossRefGoogle Scholar
  11. 11.
    Effland, T., Lawson, A., Balter, S., Devinney, K., Reddy, V., Waechter, H., Gravano, L., Hsu, D.: Discovering foodborne illness in online restaurant reviews. J. Am. Med. Inform. Assoc. (2018). Scholar
  12. 12.
    Fette, G., Ertl, M., Wörner, A., Kluegl, P., Störk, S., Puppe, F.: Information extraction from unstructured electronic health records and integration into a data warehouse. In: GI-Jahrestagung, pp. 1237–1251 (2012)Google Scholar
  13. 13.
    Fusella, E., Cilardo, A.: Minimizing power loss in optical networks-on-chip through application-specific mapping. Microprocess. Microsyst. 43, 4–13 (2016)CrossRefGoogle Scholar
  14. 14.
    Grabar, N., Zweigenbaum, P.: Automatic acquisition of domain-specific morphological resources from thesauri. In: Proceedings of RIAO, pp. 765–784. Citeseer (2000)Google Scholar
  15. 15.
    Hahn, U., Honeck, M., Piotrowski, M., Schulz, S.: Subword segmentation–leveling out morphological variations for medical document retrieval. In: Proceedings of the AMIA Symposium, p. 229. American Medical Informatics Association (2001)Google Scholar
  16. 16.
    Javanmardi, S., Shojafar, M., Shariatmadari, S., Ahrabi, S.S.: FR trust: a fuzzy reputation-based model for trust management in semantic P2P grids. Int. J. Grid Utility Comput. 6(1), 57–66 (2015)CrossRefGoogle Scholar
  17. 17.
    Lovis, C., Baud, R., Rassinoux, A.-M., Michel, P.-A., Scherrer, J.-R.: Medical dictionaries for patient encoding systems: a methodology. Artif. Intell. Med. 14(1), 201–214 (1998)CrossRefGoogle Scholar
  18. 18.
    Moore, P., Xhafa, F., Barolli, L.: Semantic valence modeling: emotion recognition and affective states in context-aware systems. In: Proceedings of the IEEE 28th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2014, pp. 536–541. IEEE (2014)Google Scholar
  19. 19.
    Norton, L.M., Pacak, M.G.: Morphosemantic analysis of compound word forms denoting surgical procedures. Methods Inf. Med. 22(1), 29–36 (1983)CrossRefGoogle Scholar
  20. 20.
    Pratt, A.W., Pacak, M.: Identification and transformation of terminal morphemes in medical English. Methods Inf. Med. 8(2), 84–90 (1969)Google Scholar
  21. 21.
    The Apache Hadoop Project: Apache HadoopGoogle Scholar
  22. 22.
    The GATE Project Team: GateGoogle Scholar
  23. 23.
    Rink, B., Harabagiu, S., Roberts, K.: Automatic extraction of relations between medical concepts in clinical texts. J. Am. Med. Inform. Assoc. 18(5), 594–600 (2011)CrossRefGoogle Scholar
  24. 24.
    Morrone, A., Bolasco, S., Baiocchi, F.: TaLTacGoogle Scholar
  25. 25.
    The Free Encyclopedia Wikipedia: TripAdvisorGoogle Scholar
  26. 26.
    The Free Encyclopedia Wikipedia: YelpGoogle Scholar
  27. 27.
    Wolff, S.: The use of morphosemantic regularities in the medical vocabulary for automatic lexical coding. Methods Inf. Med. 23(4), 195–203 (1984)CrossRefGoogle Scholar
  28. 28.
    Xhafa, F., Barolli, L.: Semantics, intelligent processing and services for big data. Fut. Gener. Comput. Syst. 37, 201–202 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Naples Federico IINaplesItaly

Personalised recommendations