Sentiment Analysis of COVID-19 Vaccine Tweets Using Machine Learning

Khakharia, Aman; Shah, Pankaj; Gupta, Priya

doi:10.2139/ssrn.3869531

Cited by 16 publications

(7 citation statements)

References 20 publications

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“…The result shows that most of the Tweets have positive opinions about Covid-19 vaccines while the least showcase negative sentiments. The findings of the current study align with the earlier studies [ 19 , 28 , 46 , 55 , 57 , 61 , 66 , 70 ] signifying that positive sentiments about Covid-19 vaccines are dominant on Twitter. The results indicate that most Twitter users are hopeful and positive about Covid-19 vaccines in India.…”

Section: Discussion and Findingssupporting

confidence: 91%

“…Sentiment analysis of social media data is emerging as an important research field and is in use at different spheres, namely, e-sports [ 35 , 36 ], sports [ 37 – 41 ] healthcare [ 42 – 44 ] and even geopolitical conflicts and crises [ 45 – 50 ]. Since the Covid-19 outbreak, a number of studies throughout the world have utilised Twitter data to assess the public opinion [ 21 , 28 , 51 – 57 ], main discussion themes [ 58 – 61 ] and misinformation [ 62 – 64 ] about the Covid-19 vaccine. Marcec and Likic [ 51 ] analysed sentiments regarding various Covid-19 vaccines employing AFINN lexicon strategy to find out the opinion of masses regarding various vaccines that were developed over a period of time for Covid-19 and concluded that people showed positive sentiments towards Pfizer and Moderna vaccines while negative sentiments developed for AstraZeneca/Oxford vaccine over time.…”

Section: Review Of Literaturementioning

confidence: 99%

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Sentiment analysis of Indian Tweets about Covid-19 vaccines

Mir

Sevukan

2022

Journal of Information Science

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People are becoming more reliant on social media networks to express their opinions about various topics and obtain health information. The study is intended to explore and analyse the sentiments of Indian people related to Covid-19 vaccines as well as to visualise the top most frequently occurring terms individuals have used to communicate their ideas on Twitter about Covid-19 vaccines in India. The Tweet Archiver was used to retrieve the Tweets against ‘Covid19vaccine’ and ‘Coronavirusvaccine’ hashtags for the period of 2 months 18 days (4 January 2021–22 March 2021). After collecting data, the Orange software and VOSviewer were used for further analysis. The Tweets were posted across the country, with an immense contribution from Maharashtra (223, 15.58%), followed by Delhi (220, 15.37%) and Tamil Nadu (73, 5.10%). The majority (639, 44.65%) of the Tweets reflect positive sentiments, followed by neutral (521, 38.50%) and negative (241, 16.84%) sentiments, respectively. This signifies that most Twitter users have a favourable opinion towards Covid vaccines in India. Based on the relevance score of the words, the words ‘Delhi heart’, ‘Lung institute’, ‘Gift’, ‘Unite2fightcorona’, and ‘Covid-19 Vaccine’ are the leading words appearing in Tweets. The study illustrates the sentiments of the Indian people towards ‘Covid-19 vaccines’, gains some insights into overall public communication about the topic and complements the existing literature. It can assist health policymakers and administrators in better understanding the polarity (positive, negative, and neutral) of Tweets about Covid-19 vaccines on Twitter to raise public awareness about health concerns and misinformation about the vaccine.

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Section: Discussion and Findingssupporting

confidence: 91%

Section: Review Of Literaturementioning

confidence: 99%

Sentiment analysis of Indian Tweets about Covid-19 vaccines

Mir

Sevukan

2022

Journal of Information Science

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“…To extract tweets from Twitter, different researcher uses different methods. Many researchers used the Twitter dataset (tweets) available on open sources (including [11], [24], [25], and [26]. Whereas some researchers extracted tweets manually (including [1], [27]) to proceed with their research.…”

Section: Data Extraction and Annotationmentioning

confidence: 99%

An ensemble approach for the identification and classification of crime tweets in the English language

Siddiqui,

Hina,

Asif

et al. 2023

Comput Sci Inf Technol

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Twitter is a famous social media platform, which supports short posts limited to 280 characters. Users tweet about many topics like movie reviews, customer service, meals they just ate, and awareness posts. Tweets carrying information about some crime scenes are crime tweets. Crime tweets are crucial and informative and separate classification is required. Identification and classification of crime tweets is a challenging task and has been the researcher’s latest interest. The researchers used different approaches to identify and classify crime tweets. This research has used an ensemble approach for the identification and classification of crime tweets. Tweepy and Twint libraries were used to collect datasets from Twitter. Both libraries use contrasting methods for extracting tweets from Twitter. This research has applied many ensemble approaches for the identification and classification of crime tweets. Logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and random forest (RF) Classifier assigned with the weights of 1,2,1,1 and 1 respectively ensemble together by a soft weighted Voting classifier along with term frequency – inverse document frequency (TF-IDF) vectorizer gives the best performance with an accuracy of 96.2% on the testing dataset.

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“…Natural Language Processing (NLP) is a technique that allows machines to "read" text by emulating the human capacity to comprehend language. This is accomplished by combining the power of artificial intelligence, computational linguistics, and computer science [5].…”

Section: Introductionmentioning

confidence: 99%

“…The task of identifying and categorizing the feelings conveyed by written expressions is known in NLP as sentiment analysis. In most cases, "positive", "negative", and "neutral" categories are considered [5]. Finding sentiments or even more fine-grained emotions in words requires sophisticated algorithms and computational methodologies.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Youth Sentiment Analysis Using Natural Language Processing

ElStohy

2023

JAIT

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Social networks are currently the most widely used platforms for exchanging thoughts on various subjects or events, particularly those geared at young people. Consequently, the Natural Language Processing (NLP) industry has access to an abundant source of data provided by those applications. This paper presents a model for sentiment analysis using Naive Bayes algorithm. A proposed model is applied to two dataset samples to train, create, and test classification models. The supervised approach combined unigram for feature extraction and the Naive Bayes algorithm to extract the trending topics for youth Tweets. The model is evaluated on a test set during the worldwide crisis and is shown to be effective in predicting opinions on new reviews. The results of the evaluation demonstrate that the model is capable of accurately predicting opinions with a high degree of accuracy.

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Sentiment Analysis of COVID-19 Vaccine Tweets Using Machine Learning

Cited by 16 publications

References 20 publications

Sentiment analysis of Indian Tweets about Covid-19 vaccines

Sentiment analysis of Indian Tweets about Covid-19 vaccines

An ensemble approach for the identification and classification of crime tweets in the English language

A Framework for Youth Sentiment Analysis Using Natural Language Processing

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