Trust Analysis for Information Concerning Food-Related Risks

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.

[1]  Stefan Schulz,et al.  Subword segmentation-leveling out morphological variations for medical document retrieval , 2001, AMIA.

[2]  Natalia Grabar,et al.  Automatic acquisition of domain-specific morphological resources from thesauri , 2000 .

[3]  Sanda M. Harabagiu,et al.  Automatic extraction of relations between medical concepts in clinical texts , 2011, J. Am. Medical Informatics Assoc..

[4]  Flora Amato,et al.  Sentiment Analysis on yelp social network , 2017, DMSVLSS.

[5]  Flora Amato,et al.  Automatic Personalization of Visiting Path Based on Users Behaviour , 2017, 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA).

[6]  Fabio Persia,et al.  Modeling recommendation as a social choice problem , 2010, RecSys '10.

[7]  A. W. Pratt,et al.  Identification and transformation of terminal morphemes in medical English part II. , 1969, Methods of information in medicine.

[8]  Fatos Xhafa,et al.  Semantic Valence Modeling: Emotion Recognition and Affective States in Context-Aware Systems , 2014, 2014 28th International Conference on Advanced Information Networking and Applications Workshops.

[9]  Walter Balzano,et al.  Logic-based clustering approach for management and improvement of VANETs , 2017, J. High Speed Networks.

[10]  Flora Amato,et al.  ABC: A knowledge Based Collaborative framework for e-health , 2015, 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

[11]  Alessandro Cilardo,et al.  Exploring the Potential of Threshold Logic for Cryptography-Related Operations , 2011, IEEE Transactions on Computers.

[12]  Luis Gravano,et al.  Discovering foodborne illness in online restaurant reviews , 2018, J. Am. Medical Informatics Assoc..

[13]  Edoardo Fusella,et al.  Minimizing power loss in optical networks-on-chip through application-specific mapping , 2016, Microprocess. Microsystems.

[14]  Giovanni Cozzolino,et al.  Using Semantic Tools to Represent Data Extracted from Mobile Devices , 2018, 2018 IEEE International Conference on Information Reuse and Integration (IRI).

[15]  L M Norton,et al.  Morphosemantic analysis of compound word forms denoting surgical procedures. , 1983, Methods of information in medicine.

[16]  Fatos Xhafa,et al.  Semantics, intelligent processing and services for big data , 2014, Future Gener. Comput. Syst..

[17]  Alessandro Cilardo,et al.  Early Prediction of Hardware Complexity in HLL-to-HDL Translation , 2010, 2010 International Conference on Field Programmable Logic and Applications.

[18]  Fabio Persia,et al.  iWIN: A Summarizer System Based on a Semantic Analysis of Web Documents , 2012, 2012 IEEE Sixth International Conference on Semantic Computing.

[19]  Mohammad Shojafar,et al.  FR trust: a fuzzy reputation-based model for trust management in semantic P2P grids , 2014, Int. J. Grid Util. Comput..

[20]  S Wolff.  The use of morphosemantic regularities in the medical vocabulary for automatic lexical coding. , 1984, Methods of information in medicine.

[21]  Christian Lovis,et al.  Medical dictionaries for patient encoding systems: a methodology , 1998, Artif. Intell. Medicine.

[22]  Son Doan,et al.  Integrating existing natural language processing tools for medication extraction from discharge summaries , 2010, J. Am. Medical Informatics Assoc..