Sentiment Analysis of Twitter Media for Public Reaction Identification on COVID-19 Monitoring System using Hybrid Feature Extraction Method

Several strategies were implemented to prevent COVID-19 spread. However, these steps were not effectively implemented in the community due to low public awareness and lack of discipline in daily life and this indicated a potential threat of continuous exposure to the virus. It was also observed that the pandemic greatly affected other areas besides the health sector ranging from the social, political, religious, and economic aspects to the resilience of the people. These can be observed through direct observation of the community or activities of the people on social media, especially in relation to the socio-economic aspect. Therefore, this research was conducted using social media, specifically Twitter, via the Twitter API to obtain data related to COVID-19 pandemic in Indonesia. In this research, a sentiment analysis method was developed in this research to identify public opinion related to the spread of the COVID-19 virus and its social impact on society. This was achieved using the TF-IDF and Lexical methods for feature extraction and Naive Bayes for classification. This research used a dataset obtained through Twitter using the keyword "COVID-19" in Indonesian and manually labelled using 5 categories of reactions i.e., fear, angry, love, sad, and happy. The prediction accuracy values showed that the proposed TFBS method had a higher accuracy value of 0.85 compared to other methods. The performance was also evaluated by calculating the precision, recall, and F-score values for each extraction method used, and the proposed TFBS method was observed to have the highest values while ME had the lowest. © Ismail Saritas. All rights reserved.

Sentiment Analysis of Public Reaction to COVID19 in Twitter Media using Naïve Bayes Classifier

Currently, the world's attention was focused on the disease outbreak, namely the corona virus (COVID19). World Health Organization (WHO) declare that this virus was a global pandemic in all countries. The various impacts that arise due to this virus cover various fields, namely health, social, political, religious, economic to resilience and security. Some of the services currently used were still focused on the health sector, namely in the form of treatment and information services related to the development of the spread of the virus. This research will develop a service that was used to identify social impacts in the community through observing community activities on social media, namely Twitter, in the form of an analysis of the public's reaction to COVID19. Through this Twitter, a data acquisition process will be carried out to obtain data related to COVID19 which will then be carried out a sentiment analysis using the Naïve Bayes method so that the results of the public reaction sentiment will be obtained. The experimental result shows that prediction accuracy was 0,86. Furthermore, the results of the Recall was 0,687, the precision was 0,827 and the F-Score was 0.749 © 2021 IEEE.