Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156348
Title: Sentic computing for HIV prevention and care
Authors: Low, Valerian Qin Ling
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Low, V. Q. L. (2022). Sentic computing for HIV prevention and care. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156348
Project: SCSE21-0230
Abstract: The Action for AIDS (AFA) has established a community roadmap towards eradicating AIDS and HIV in Singapore by 2030. However, the results in Singapore now, despite being almost achieving the “90-90-90” goals set by the UNAIDS, still fall short of achieving the target that “90% of people living with HIV (PLHIV) will be aware of their HIV status”. In this paper, we will be performing sentiment analysis on interviews with PLHIV, which allows us to extract the sentiment polarity and emotions, thus being able to better understand the current HIV situation in Singapore. We have developed a complete sentiment analysis system that automatically processes the data and stores the results. We used the APIs available from SenticNet and attempted to improve the accuracy using Multi-Task Learning.
URI: https://hdl.handle.net/10356/156348
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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