Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm

Pristiyono,; Ritonga, Mulkan; Ihsan, Muhammad Ali Al; Anjar, Agus; Rambe, Fauziah Hanum

doi:10.1088/1757-899x/1088/1/012045

Cited by 73 publications

(48 citation statements)

References 11 publications

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“…The frequency with which the word appears in papers demonstrates its popularity. Thus, the weight of the relationship between a word and a document will be greater if the term occurs frequently in the document and the document collection as a whole has few instances of the word [11]. The TF-IDF value is calculated by multiplying the two equations as follows:…”

Section: Term Frequency -Inverse Document Frequency (Tf-idf)mentioning

confidence: 99%

Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia

Riyanto

Azis

2021

J. Appl. Data Sci.

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According to the Indonesian government, Indonesia has been afflicted by Covid-19 since March 2, 2020. Numerous countries, including Indonesia, have made efforts, but with the spread of perceptions, rumors, and a flood of information into the society regarding vaccines, there are both advantages and disadvantages to vaccines. government-led immunization campaigns. As a result, it is vital to examine public sentiment toward the government's immunization programs. The goal of this study is to ascertain the emotion toward the Covid-19 vaccination in Indonesia based on the classification results. The Support Vector Machine classification technique was employed in this investigation (SVM). The SVM classification method was chosen because it possesses the ability to generalize when it comes to identifying a pattern, excluding the data used in the method's learning phase. Classification with an SVM linear kernel and TF-IDF weighting, as well as data sharing via K-fold cross validation with a value of k=10. Positive and negative classifications are made. Following preprocessing and classification, we determined the f1 values, accuracy, precision, and recall to use as reference values when evaluating the classification. SVM performed well in classifying the data in this investigation, with f1 = 88.7%, accuracy = 84.4%, precision = 86.2%, and recall = 97%. This value is acceptable, and hence SVM is suitable for identifying sentiment data about the Covid-19 vaccine in Indonesia. Additional study can be conducted with richer tweet data, more thorough preprocessing, and comparison to other classification algorithms to obtain a higher categorization evaluation score.

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Section: Term Frequency -Inverse Document Frequency (Tf-idf)mentioning

confidence: 99%

Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia

Riyanto

Azis

2021

J. Appl. Data Sci.

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“…Researchers make use of these opinions to analyze the sentiments of peoples on various issues like politics, sports, product reviews etc., The emotions expressed by the Twitter community during 2020 has been used to understand the people sentiments during a pandemic [7][8][9][10][11][12].…”

Section: Connect To Twitter and Authenticationmentioning

confidence: 99%

#Vaccine: Using Hashtags from Indian Tweets to Capture and Analyse the Sentiments of People on Vaccination for Covid’19 Pandemic

Anuratha¹,

Sujeetha²,

Nandhini³

et al. 2021

Recent Trends in Intensive Computing

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To prevent the public from pandemic Covid’19 the government of India has started the vaccination from mid of January 2021. The government has approved the two vaccines, Covishield from the university of Oxford and Covaxin from Bharat Biotech.The vaccination started with frontline workers and is further extended to common public prioritizing the elders of above 60 years and people aged 45 years above with co morbidities. Though many people have got benefitted from it there is still a group of people not convinced with the vaccination. We have carried out this work to analyze those Indian people sentiments on the vaccines through the hash tags of tweets. The results show that though majority of the community has a positive belief on the vaccines but some of them still express negative emotions.

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“…It is found that fear is significant in the discussion about covid-19. In the paper [5], Twitter data is extracted manually by data crawling using Twitter API access token with "Vaccine" and "COVID-19" as keywords. Naïve Bayes algorithm is used for sentiment analysis and is found that the majority of the tweets have a negative sentiment.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

Shamrat

Chakraborty

Imran³

et al. 2021

IJEECS

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The pandemic has taken the world by storm. Almost the entire world went into lockdown to save the people from the deadly COVID-19. Scientists around the around have come up with several vaccines for the virus. Amongthem, Pfizer, Moderna, and AstraZeneca have become quite famous. General people however have been expressing their feelings about the safety and effectiveness of the vaccines on social media like Twitter. In this study, such tweets are being extracted from Twitter using a Twitter API authentication token. The raw tweets are stored and processed using NLP. The processed data is then classified using a supervised KNN classification algorithm. The algorithm classifies the data into three classes, positive, negative, and neutral. These classes refer to the sentiment of the general people whose Tweets are extracted for analysis. From the analysis it is seen that Pfizer shows 47.29%positive, 37.5% negative and 15.21% neutral, Moderna shows 46.16%positive, 40.71% negative, and 13.13% neutral, AstraZeneca shows 40.08%positive, 40.06% negative and 13.86% neutral sentiment.

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Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm

Cited by 73 publications

References 11 publications

Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia

Application of the Vector Machine Support Method in Twitter Social Media Sentiment Analysis Regarding the Covid-19 Vaccine Issue in Indonesia

#Vaccine: Using Hashtags from Indian Tweets to Capture and Analyse the Sentiments of People on Vaccination for Covid’19 Pandemic

Sentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm

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