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dc.contributor.advisorNouman, Mohammad
dc.contributor.authorAlsalem, Fatmah
dc.contributor.authorNassir, Jana
dc.contributor.authorBawazir, Joud
dc.date.accessioned2023-07-05T09:05:36Z
dc.date.available2023-07-05T09:05:36Z
dc.date.submitted2023-07-01
dc.identifier.urihttp://hdl.handle.net/20.500.14131/934
dc.description.abstractSentiment analysis is critical for comprehending people’s opinions and attitudes regarding fi- nancial applications. The study offered a thorough approach to sentiment analysis utilizing machine learning and deep learning models in this research. Data pretreatment procedures, model imple- mentation, and evaluation are all part of the process. Using the Keras and TensorFlow frameworks, we constructed numerous machine learning and deep learning models such as Naive Bayes, SVM, Decision Tree, BERT, and RNN. The accuracy and F1 score measures were used to evaluate the performance of these models. A thematic analysis was also performed to uncover common themes and subjects in financial application reviews. The findings show that sentiment analysis is useful in analyzing user sentiments and providing insights for improving user experienceen_US
dc.language.isoen_USen_US
dc.subjectMachine Learningen_US
dc.subjectSentiment Analysisen_US
dc.subjectDeep Learningen_US
dc.subjectKerasen_US
dc.titleUsing Machine Learning and Sentiment Analysis Methods to Evaluate Financial Apps Based on User Reviewsen_US
dc.typeStudent Projecten_US
refterms.dateFOA2023-07-01T00:00:00Z
dc.contributor.departmentComputer Scienceen_US


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