Trust Analysis for Information Concerning Food-Related Risks
Abstract
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.
Notes
Acknowledgment
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.
References
- 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.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.Amato, F., Cozzolino, G., Mazzeo, A., Pizzata, A.: Sentiment analysis on Yelp social network, pp. 92–98 (2017)Google Scholar
- 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.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.Cilardo, A.: Exploring the potential of threshold logic for cryptography-related operations. IEEE Trans. Comput. 60(4), 452–462 (2011)MathSciNetCrossRefGoogle Scholar
- 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.Cozzolino, G.: Using semantic tools to represent data extracted from mobile devices, pp. 530–536 (2018)Google Scholar
- 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.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.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). https://doi.org/10.1093/jamia/ocx093CrossRefGoogle Scholar
- 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.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.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.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.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.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.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.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.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.The Apache Hadoop Project: Apache HadoopGoogle Scholar
- 22.The GATE Project Team: GateGoogle Scholar
- 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.Morrone, A., Bolasco, S., Baiocchi, F.: TaLTacGoogle Scholar
- 25.The Free Encyclopedia Wikipedia: TripAdvisorGoogle Scholar
- 26.The Free Encyclopedia Wikipedia: YelpGoogle Scholar
- 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.Xhafa, F., Barolli, L.: Semantics, intelligent processing and services for big data. Fut. Gener. Comput. Syst. 37, 201–202 (2014)CrossRefGoogle Scholar