Open AccessSentiment analysis on twitter tweets about COVID-19 vaccines usi ng NLP and supervised KNN classification algorithm
Author(s) -
F. M. Javed Mehedi Shamrat,
Sovon Chakraborty,
Mubashir Imran,
Jannatun Naeem Muna,
Md. Masum Billah,
Protiva Das,
Osmud Rahman
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v23.i1.pp463-470
Subject(s) - sentiment analysis , artificial intelligence , social media , computer science , security token , support vector machine , covid-19 , lexical analysis , natural language processing , machine learning , world wide web , medicine , computer security , disease , pathology , infectious disease (medical specialty)
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.