RT Journal Article
T1 Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics
A1 Velasco-López, Jorge-Eusebio
A1 Carrasco González, Ramón Alberto
A1 Serrano-Guerrero, Jesús
A1 Chiclana, Francisco
AB Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others.Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implementedusing methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and timeseries prediction with the Prophet model. As a practical demonstration of the proposed model, weuse tweets as data source (from the social network X, formerly known as Twitter) generated duringthe COVID-19 pandemic lockdown period in Spain, which are processed to identify the overallsentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined withofficial statistical indicators for prediction, visualizing the results through dashboards.
PB MDPI
YR 2024
FD 2024
LK https://hdl.handle.net/20.500.14352/114401
UL https://hdl.handle.net/20.500.14352/114401
LA eng
NO Velasco-López, J.-E.; Carrasco, R.-A.; Serrano-Guerrero, J.; Chiclana, F. Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics. Mathematics 2024, 12, 911. https://doi.org/ 10.3390/math1206091
DS Docta Complutense
RD 30 dic 2025