Sentiment analysis of Twitter data to analyze the effect of COVID-19

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dc.contributor.authorAdeola Ayandeyi
dc.date.accessioned2025-05-01T16:10:51Z
dc.date.available2025-05-01T16:10:51Z
dc.date.issued2021-09-01
dc.descriptionCoronavirus pandemic has caused major changes in peoples’ personal and social lives. The psychological effects have been substantial because it has affected the ways people live, work, and even socialize. It has also become major discussions on social media platforms as people showcase their opinions and the effect of the virus on their mental health particularly. This pandemic is the first of its kind as humans has never encountered anything like this virus and this is due to its airborne characteristics which leads to social distancing. Before the virus surfaced, some countries of the world were dealing with mental health cases, with over 40 percent of adults in the USA reported experiencing mental health challenges, including anxiety and depression. Social media has become one of the major sources of information due to information sharing on a very large scale. Peoples’ perception and emotions are also portrayed through their conversations. In this research work, the interaction and conversation of people on social media most especially Twitter as it relates with the pandemic and its effect on mental health, will be analyzed using machine learning tools and algorithms. This analysis will help suggest the area of concentration to medical practitioners in order to speed up the diagnosis and recovery process and reduce the mental health issues which has escalated due to the virus.
dc.identifier.doihttps://doi.org/10.7939/r3-nj2e-vv14
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectCoronavirus
dc.subjectCOVID-19
dc.subjectMental Health
dc.subjectSentimental analysis
dc.subjectTwitter Data
dc.subjectData Preprocessing
dc.subjectmachine learning classifiers
dc.subjectSupervised learning
dc.subjectSemi-supervised learning
dc.subjectperformance metrics
dc.titleSentiment analysis of Twitter data to analyze the effect of COVID-19
dc.typehttp://purl.org/coar/resource_type/c_1843
ual.jupiterAccesshttp://terms.library.ualberta.ca/public

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Master of Science in Information Technology Project Reports (Concordia University of Edmonton)