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dc.contributor.author
Anastasiou, Panagiota
el
dc.date.accessioned
2023-06-09T11:14:00Z
dc.date.available
2023-06-09T11:14:00Z
dc.date.issued
2023-06-09
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/30285
dc.rights
Default License
dc.subject
Healthcare products
en
dc.subject
Sentiment analysis
el
dc.subject
Topic modeling
el
dc.subject
YouTube comments
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dc.title
YouTube Sentiment Analysis and Topic Modeling on Healthcare Products
en
heal.type
masterThesis
en_US
heal.dateAvailable
2023
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in Data Science
en_US
heal.publicationDate
2023
heal.abstract
This dissertation was written as a part of the MSc in Data Science at the International Hellenic University. Some of the most well-liked websites in the world are social media platforms, such as YouTube, that provide everyone with a voice and the ability to express their thoughts and feelings. Sentiment analysis may be used to retrieve and measure these users' thoughts and feelings. This study uses a hybrid approach that combines learning-based and lexicon-based methodologies to achieve better results. The comments are labeled using general-purpose sentiment lexicons like TextBlob, VADER and Flair. Furthermore, the sentiments of the comments are classified using the learning models Logistic Regression (LR), Multinomial Naive Bayes (Multi.NB), Random Forest (RF), Support Vector Machine (SVM), and Stochastic Gradient Descent Classifier (SGD Classifier). The algorithms' performance is evaluated using accuracy, precision, recall, and F1- score. Results from TextBlob are encouraging, with an accuracy of 91% when using SVM. Finally, topic modeling was applied to extract information about the content of the comments. Five dominant topics were spotted, that refer to what the users feel about the commercials.
el
heal.advisorName
Tzafilkou, Katerina
el
heal.committeeMemberName
Koukaras, Paraskevas
el
heal.committeeMemberName
Vretos, Nikolaos
el
heal.academicPublisher
IHU
el
heal.academicPublisherID
ihu
en_US


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