About: Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America
Creator	Cánovas-García, Mar Valencia-García, Rafael Antonio García-Díaz, José
Source	Elsevier; Medline; PMC
abstract	Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public’s opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.
has issue date	2020-06-18(xsd:dateTime)
bibo:doi	10.1016/j.future.2020.06.019
bibo:pmid	32572291
has license	no-cc
sha1sum (hex)	da82c370db312b83924beda1fd298a727ba694cb
schema:url	https://doi.org/10.1016/j.future.2020.06.019
resource representing a document's title	Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America
has PubMed Central identifier	PMC7301140
has PubMed identifier	32572291
schema:publication	Future Gener Comput Syst
resource representing a document's body	covid:da82c370db312b83924beda1fd298a727ba694cb#body_text
is schema:about of	named entity 'public health' named entity 'policies' named entity 'expressed' named entity 'order' named entity 'viruses' named entity 'Multilayer perceptron' named entity 'This' named entity 'comprehensive' named entity 'measure' named entity 'embeddings' named entity 'concepts' named entity 'identification' named entity 'PROPOSAL' named entity 'OTHER STRUCTURES' named entity 'RISKS' named entity 'AVAILABLE' named entity 'USERS' named entity 'OTHER THAN' named entity 'BASIC' named entity 'CONCEPT' named entity 'TO PROVIDE' named entity 'Journal' named entity 'infectious disease' named entity 'entities' named entity 'Once' named entity 'general' named entity 'nature' named entity 'web platform' named entity 'complexity' named entity 'order' named entity 'infectious disease' named entity 'capture' named entity 'basic' named entity 'sentences' named entity 'domain' named entity 'aspect' named entity 'provide' named entity 'Journal' named entity 'Journal' named entity 'consists' named entity 'measure' named entity 'sentiment analysis' named entity 'Dengue' named entity 'deep-learning' named entity 'features' named entity 'feed-forward' named entity 'infectious disease' named entity 'sentence embedding' named entity 'NLP' named entity 'Multilayer perceptron' named entity 'artificial neural networks' named entity 'Infodemiology' named entity 'perceptrons' named entity 'NLP' named entity 'sentiment analysis' named entity 'public health' named entity 'Natural Language Processing' named entity 'Journal' named entity 'word embeddings' named entity 'domain ontology' named entity 'infectious disease' named entity 'NLP' named entity 'max pooling' named entity 'Dengue' named entity 'neural network' named entity 'fever' named entity 'Argentina' named entity 'infectious disease' named entity 'machine-learning' named entity 'LSTM' named entity 'natural language' named entity 'Pragmatics' named entity 'vector' named entity 'machine-learning' named entity 'statistical methods' named entity 'semantic relatedness' named entity 'feature extraction' named entity 'feature engineering' named entity 'semantic similarity'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software