Sentiment analysis for COVID-19 vaccination news
In this digital era where information is widely available and easily retrievable from many different sources, large amount of unstructured data is generated every day. Social media, such as Facebook, Instagram or Twitter, provides a means for people around the world to create, share and exchange...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2022
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Online Access: | https://hdl.handle.net/10356/156559 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | In this digital era where information is widely available and easily retrievable from many
different sources, large amount of unstructured data is generated every day. Social media, such
as Facebook, Instagram or Twitter, provides a means for people around the world to create,
share and exchange information and ideas in virtual communities and networks has embedded
itself in a large percentage of human population’s everyday lives.
In 2020, the outbreak of Covid-19 coronavirus has led to a sharp increase in the usage of social
media and as such, news related to both the virus and its vaccines are generated in vast amounts
daily. This provides opportunity for the data to be mined and turned into meaningful digital
outputs through the use of sentiment analysis. Sentiment analysis, also known as opinion
mining is the use of natural language processing, text analysis, computational linguistics to
systematically identify, extract, quantify, and study affective states and subjective information
expressed by a person towards a topic or a phenomenon.
This project aims to use sentiment analysis to identify the various sentiments of a given text or
news related to COVID-19 on social media platforms such as Facebook and Twitter. Various
unsupervised machine learning algorithms, such as Vader(Valence Aware Dictionary for
Sentiment Reasoning), Textblob and also pretrained BERT model on IMDB data are used to
label messages by performing text classification. These classifiers will classify the text into
three different sentiments positive, neutral and negative and will be compared against the
sentiments that are manually labelled. |
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