Name
Abstract | ||
|---|---|---|
Data analysis is increasingly becoming a fundamental competence for the success of companies, but also in many social phenomena. Particular attention deserves the health sector because compared to others, and especially compared to the economic one, it has fewer case studies in which the social network analysis has been applied and, therefore, its potential has not yet been fully understood and adequately exploited. 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 [1], on which we perform the textual analysis foreseen in the proposed methodology. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-15032-7_102 | AINA |
Field | DocType | Citations |
Health sector,Data science,Social network,Social media,Textual information,Computer science,Social network analysis,Computer network,Perception,Food safety | Conference | 0 |
PageRank | References | Authors |
0.34 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandra Amato | 1 | 0 | 3.38 |
| Walter Balzano | 2 | 48 | 14.23 |
| Giovanni Cozzolino | 3 | 3 | 15.60 |
| Francesco Moscato | 4 | 298 | 32.28 |