Analysis of citizen's sentiment towards Philippine administration's intervention against COVID-19

Abstract

The COVID-19 pandemic affected the world. The World Health Organization or WHO issued guidelines the public must follow to prevent the spread of the disease. This includes social distancing, the wearing of facemasks, and regular washing of hands. These guidelines served as the basis for formulating policies by countries affected by the pandemic. In the Philippines, the government implemented different initiatives, following the guidelines of WHO, that aimed to mitigate the effect of the pandemic in the country. Some of the initiatives formulated by the administration include international and domestic travel restrictions, community quarantine, suspension of face-to-face classes and work arrangements, and phased reopening of the Philippine economy to name a few. The initiatives implemented by the government during the surge of COVID-19 disease have resulted in varying reactions from the citizens. The citizens expressed their reactions to these initiatives using different social media platforms such as Twitter and Facebook. The reactions expressed using these social media platforms were used to analyze the sentiment of the citizens towards the initiatives implemented by the government during the pandemic. In this study, a Bidirectional Recurrent Neural Network-Long Short-term memory - Support Vector Machine (BRNN-LSTM-SVM) hybrid sentiment classifier model was used to determine the sentiments of the Philippine public toward the initiatives of the Philippine government to mitigate the effects of the COVID-19 pandemic. The dataset used was collected and extracted from Facebook and Twitter using API and www.exportcomments.com from March 2020 to August 2020. 25% of the dataset was manually annotated by two human annotators. The manually annotated dataset was used to build the COVID-19 context-based sentiment lexicon, which was later used to determine the polarity of each document. Since the dataset contained unstructured and noisy data, preprocessing activities such as conversion to lowercase characters, removal of stopwords, removal of usernames and pure digit texts, and translation to the English language were performed. The preprocessed dataset was vectorized using Glove word embedding and was used to train and test the performance of the proposed model. The performance of the Hybrid BRNN-LSTM-SVM model was compared to BRNN-LSTM and SVM by performing experiments using the preprocessed dataset. The results show that the Hybrid BRNN-LSTM-SVM model, which gained 95% accuracy for the Facebook dataset and 93% accuracy for the Twitter dataset, outperformed the Support Vector Machine (SVM) sentiment model whose accuracy only ranges from 89% to 91% for both datasets. The results indicate that the citizens harbor negative sentiments towards the initiatives of the government in mitigating the effect of the COVID-19 pandemic. The results of the study may be used in reviewing the initiatives imposed during the pandemic to determine the issues which concern the citizens. This may help policymakers formulate guidelines that may address the problems encountered during a pandemic. Further studies may be conducted to analyze the sentiment of the public regarding the implementation of limited face-to-face classes for tertiary education, implementing lesser restrictions, vaccination programs in the country, and other related initiatives that the government continues to implement during the COVID-19 pandemic.