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Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious
José Antonio García-Díaz1, Mar Cánovas-García1, Rafael Valencia-García1
1Departamento de Informática y Sistemas, Universidad de Murcia, 30100, Murcia, Spain.
Future Generations Computer Systems : FGCS
|June 24, 2020
Summary
Infodemiology uses ontology-driven sentiment analysis to understand public opinion on infectious diseases like Zika and Dengue in Spanish social media. This approach improves public health communication and detection systems.
Area of Science:
- Public Health
- Computational Linguistics
- Infodemiology
Background:
- Infodemiology mines public health data from unstructured text.
- Challenges include data complexity and limited NLP resources for non-English languages.
- Existing methods struggle to interpret public sentiment on infectious diseases effectively.
Purpose of the Study:
- To propose an ontology-driven aspect-based sentiment analysis model.
- To measure public opinion on infectious diseases in Spanish.
- To address limitations in understanding public health information from diverse linguistic sources.
Main Methods:
- Developed an ontology to model infectious disease concepts (risks, symptoms, drugs).
- Measured concept relationships to understand influence on sentiment.
- Applied deep learning models for aspect-based sentiment analysis on Spanish tweets about Zika, Dengue, and Chikungunya.
Main Results:
- Successfully modeled infectious disease concepts and their interrelationships.
- Built a sentiment analysis model capable of analyzing Spanish text.
- Demonstrated a method to gauge public sentiment towards specific disease-related concepts.
Conclusions:
- Ontology-driven sentiment analysis is effective for understanding public health opinions in Spanish.
- The proposed model enhances infodemiology by providing nuanced insights into public perception of infectious diseases.
- This approach can improve public health communication and early detection strategies.
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