Comparing Text Representations: In Search Of Caring Communities

dc.rights.license	CC-BY-NC-ND
dc.contributor.advisor	Nguyen, Dong
dc.contributor.author	Said, Kalee Said
dc.date.accessioned	2024-07-24T23:04:03Z
dc.date.available	2024-07-24T23:04:03Z
dc.date.issued	2024
dc.identifier.uri	https://studenttheses.uu.nl/handle/20.500.12932/46868
dc.description.abstract	Multiple text representation techniques ( BERT, word2vec, LDA topics etc) are compared for a text classification task. This classification task involves identifying caring communities from Dutch Chamber of Commerce data and utilizes a RF classifier. The goal is to identify the highest performing text representation. The classifier using the Word2Vec representation ends up with the highest F1-score.
dc.description.sponsorship	Utrecht University
dc.language.iso	EN
dc.subject	Multiple text representation techniques ( BERT, word2vec, LDA topics etc) are compared for a text classification task. This classification task involves identifying caring communities from Dutch Chamber of Commerce data. The goal is to identify the highest performing text representation.
dc.title	Comparing Text Representations: In Search Of Caring Communities
dc.type.content	Master Thesis
dc.rights.accessrights	Open Access
dc.subject.keywords	NLP;BERT;ADS;ML;
dc.subject.courseuu	Applied Data Science
dc.thesis.id	34871