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dc.contributor.advisorTerri Hoareen
dc.contributor.authorModha, Yash Vijay
dc.date.accessioned2021-10-26T14:17:45Z
dc.date.available2021-10-26T14:17:45Z
dc.date.issued2021
dc.identifier.citationModha, Y.V. (2021). Machine Learning to aid mental health among youth during COVID-19. Masters Thesis. Dublin Business School.en
dc.identifier.urihttps://esource.dbs.ie/handle/10788/4292
dc.description.abstractThis research presents a comparative study of specialized deep learning and state of the art machine learning approaches for multiclass classification of six emotions: joy, sadness, surprise, fear, love, anger. Specialized deep learning algorithm Bi-LSTM; state of the art H2O ANN; traditional SVM and state of the art H2O Gradient Boosting Machines are applied on vectorised text. Cross validated performance using different vectorization techniques: text to sequences, word to vectors and TF-IDF are presented. The specialized Bi-LSTM on text to sequence vectorised data outperforms SVM and both outperform H2O ANN. A Gradient Boosted Machine age classifier is used to stratify test data. The traditional TF-IDF applied SVM outperforms the Bi-STM model on both Youth and Adult test data. The research is further extended to present a chatbot deployment of the emotion classifier.en
dc.language.isoenen
dc.publisherDublin Business Schoolen
dc.rights.urihttp://esource.dbs.ie/copyrighten
dc.subjectMachine learningen
dc.subjectSentiment analysisen
dc.subjectMental healthen
dc.titleMachine Learning to aid mental health among youth during COVID-19en
dc.typeThesisen
dc.rights.holderItems in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.en


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