A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia

The increasing use of social media platform like Twitter provides opportunity for information dissemination to the public. The Dengvaxia controversy in the Philippines negatively affected the public's perception towards vaccination. It has been noted that due to this incident, many parents have...

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Main Authors: Abrigo, Angelu Bianca C, Estuar, Ma. Regina Justina E
Format: text
Published: Archīum Ateneo 2019
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/184
https://ieeexplore.ieee.org/abstract/document/8711432?casa_token=EBnZFNe120cAAAAA:je9TRhH5QClSQrj1QG-_fWbsenBlFNehHBu-pVyuNDW6-sIIP_DPHdHg4ElYsJLzsRVCtLafoAY
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Institution: Ateneo De Manila University
id ph-ateneo-arc.discs-faculty-pubs-1183
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spelling ph-ateneo-arc.discs-faculty-pubs-11832020-07-08T05:30:55Z A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia Abrigo, Angelu Bianca C Estuar, Ma. Regina Justina E The increasing use of social media platform like Twitter provides opportunity for information dissemination to the public. The Dengvaxia controversy in the Philippines negatively affected the public's perception towards vaccination. It has been noted that due to this incident, many parents have decided not to have their children vaccinated due to fear of endangering them [2]. This resulted to children contracting other diseases like measles due to the lack of immunization [1] [2]. The preference to not have newborns undergo vaccination program remains a threat to public health. Using publicly accessible tweets, this study aims to understand health perceptions of the public in relation to Dengvaxia. A deep neural network approach using n-gram vectorization is used in comparison to the Doc2Vec neural network classifier to identify tweets containing personal perception on health. It was discovered that not only does the bigram model perform better in classifying than the Doc2Vec model with a performance measure of 86.25% accuracy, 0.85 precision, 0.86 ROC and 0.85 F1 score, but also it is able to identify clearer and more diverse topic using LDA topic modeling in comparison with unigram and trigram model. This method allows the monitoring of public perception and acceptance towards the implementation of a new medication or vaccination especially after the Dengvaxia scandal that the Philippines experienced. 2019-01-01T08:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/184 https://ieeexplore.ieee.org/abstract/document/8711432?casa_token=EBnZFNe120cAAAAA:je9TRhH5QClSQrj1QG-_fWbsenBlFNehHBu-pVyuNDW6-sIIP_DPHdHg4ElYsJLzsRVCtLafoAY Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo deep neural network N-gram natural language processing text mining Computer Sciences Health Information Technology
institution Ateneo De Manila University
building Ateneo De Manila University Library
country Philippines
collection archium.Ateneo Institutional Repository
topic deep neural network
N-gram
natural language processing
text mining
Computer Sciences
Health Information Technology
spellingShingle deep neural network
N-gram
natural language processing
text mining
Computer Sciences
Health Information Technology
Abrigo, Angelu Bianca C
Estuar, Ma. Regina Justina E
A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia
description The increasing use of social media platform like Twitter provides opportunity for information dissemination to the public. The Dengvaxia controversy in the Philippines negatively affected the public's perception towards vaccination. It has been noted that due to this incident, many parents have decided not to have their children vaccinated due to fear of endangering them [2]. This resulted to children contracting other diseases like measles due to the lack of immunization [1] [2]. The preference to not have newborns undergo vaccination program remains a threat to public health. Using publicly accessible tweets, this study aims to understand health perceptions of the public in relation to Dengvaxia. A deep neural network approach using n-gram vectorization is used in comparison to the Doc2Vec neural network classifier to identify tweets containing personal perception on health. It was discovered that not only does the bigram model perform better in classifying than the Doc2Vec model with a performance measure of 86.25% accuracy, 0.85 precision, 0.86 ROC and 0.85 F1 score, but also it is able to identify clearer and more diverse topic using LDA topic modeling in comparison with unigram and trigram model. This method allows the monitoring of public perception and acceptance towards the implementation of a new medication or vaccination especially after the Dengvaxia scandal that the Philippines experienced.
format text
author Abrigo, Angelu Bianca C
Estuar, Ma. Regina Justina E
author_facet Abrigo, Angelu Bianca C
Estuar, Ma. Regina Justina E
author_sort Abrigo, Angelu Bianca C
title A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia
title_short A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia
title_full A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia
title_fullStr A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia
title_full_unstemmed A Comparative Analysis of N-Gram Deep Neural Network Approach to Classifying Human Perception on Dengvaxia
title_sort comparative analysis of n-gram deep neural network approach to classifying human perception on dengvaxia
publisher Archīum Ateneo
publishDate 2019
url https://archium.ateneo.edu/discs-faculty-pubs/184
https://ieeexplore.ieee.org/abstract/document/8711432?casa_token=EBnZFNe120cAAAAA:je9TRhH5QClSQrj1QG-_fWbsenBlFNehHBu-pVyuNDW6-sIIP_DPHdHg4ElYsJLzsRVCtLafoAY
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