Exploring User-Generated Content to Detect Community Problems: The Ontological Model of ALLEGRO

Carlos Periñán-Pascual

2023

Abstract

Social-media services contribute to creating situation awareness, thus offering a snapshot of today’s society. Citizens can use such communication channels to report problems concerning the quality of life of individuals and the well-being of the community in which they live. Therefore, we can develop applications that can analyse online user-generated data about a variety of problems from different topics (e.g. education, health, or politics, among many others) to reconstruct the state of society as interpreted by social-media users in the given community. In this context, the main objective of this paper is to describe the ontological model required for representing community problems affecting quality of life and well-being, and how this ontology supports the natural language processing and text-mining tasks of topic categorisation and keyword extraction. This ontological model can become a significant component in natural language understanding applications, particularly in those where machine-learning or neural-network models are enhanced with external knowledge to perform opinion mining.

Paper Citation

in Harvard Style

Periñán-Pascual C. (2023). Exploring User-Generated Content to Detect Community Problems: The Ontological Model of ALLEGRO. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD; ISBN 978-989-758-671-2, SciTePress, pages 224-230. DOI: 10.5220/0012203300003598

in Bibtex Style

@conference{keod23,
author={Carlos Periñán-Pascual},
title={Exploring User-Generated Content to Detect Community Problems: The Ontological Model of ALLEGRO},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD},
year={2023},
pages={224-230},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012203300003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD
TI - Exploring User-Generated Content to Detect Community Problems: The Ontological Model of ALLEGRO
SN - 978-989-758-671-2
AU - Periñán-Pascual C.
PY - 2023
SP - 224
EP - 230
DO - 10.5220/0012203300003598
PB - SciTePress