dor_id: 4142999

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Journal of Applied Research and Technology", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Scopus, Directory of Open Access Journals (DOAJ); Sistema Regional de Información en Línea para Revistas Científicas de América Latina, el Caribe, España y Portugal (Latindex); Indice de Revistas Latinoamericanas en Ciencias (Periódica); La Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc); Consejo Nacional de Ciencia y Tecnología (CONACyT); Google Scholar Citation

561.#.#.u: https://www.icat.unam.mx/

650.#.4.x: Ingenierías

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/868/944

100.1.#.a: Noferesti, Samira; Shamsfard, Mehrnoush

524.#.#.a: Noferesti, Samira, et al. (2022). A Semantic Framework based on Domain Knowledge for Opinion Mining of Drug Reviews. Journal of Applied Research and Technology; Vol. 20 Núm. 6, 2022; 652-667. Recuperado de https://repositorio.unam.mx/contenidos/4142999

245.1.0.a: A Semantic Framework based on Domain Knowledge for Opinion Mining of Drug Reviews

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2022

264.#.1.c: 2022-12-23

653.#.#.a: Opinion mining; drug reviews; indirect opinions; domain knowledge; ambiguous concepts

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/868

001.#.#.#: 074.oai:ojs2.localhost:article/868

041.#.7.h: eng

520.3.#.a: Opinion mining has been attracting increasing attention in recent years. Existing approaches to opinion mining that have worked on general domains face two major challenges for polarity classification of drug reviews. Firstly, indirect opinions frequently occur in the drug domain, while the existing methods have mainly focused on direct opinions and ignored the indirect ones. Secondly, previous works are not sufficient for polarity classification of ambiguous concepts in the drug domain. This paper proposed a semantic framework based on domain knowledge for resource construction and exploitation for indirect opinion mining of drug reviews. Accordingly, some methods were introduced, developed, and compared for building and exploiting a combined knowledge base, polarity-tagged corpus, and context-aware resources for the polarity detection of drug reviews. The test results showed that the proposed methods reached a precision of 89.18% and 80.4% in the application of the combined knowledge base and the polarity-tagged corpus for polarity detection of indirect opinions, respectively. Also, a precision of 79.93% was achieved with the use of context-aware resources constructed for the polarity detection of ambiguous concepts. Overall, the results obtained demonstrated the performance of the proposed methods compared to the existing methods.

773.1.#.t: Journal of Applied Research and Technology; Vol. 20 Núm. 6 (2022); 652-667

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

300.#.#.a: Páginas: 652-667

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.24486736e.2022.20.6.868

harvesting_date: 2023-11-08 13:10:00.0

856.#.0.q: application/pdf

file_creation_date: 2022-12-22 22:19:21.0

file_modification_date: 2022-12-22 22:19:21.0

file_creator: Yolanda G.G.

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last_modified: 2024-03-19 14:00:00

license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es

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