Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157406
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dc.contributor.authorFarhan Khalifa Ibrahimen_US
dc.date.accessioned2022-05-14T13:01:33Z-
dc.date.available2022-05-14T13:01:33Z-
dc.date.issued2022-
dc.identifier.citationFarhan Khalifa Ibrahim (2022). True language understanding for an explainable AI system. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157406en_US
dc.identifier.urihttps://hdl.handle.net/10356/157406-
dc.description.abstractMillions of messages and thousands of articles are posted every day, and this information is stored in an unstructured natural text. Natural Language Processing (NLP) is a study to understand text using computational techniques. One of the most important tasks in NLP is sentiment analysis which studies people’s opinions, emotions, and attitudes. Sentiment analysis is a challenging task involving context understanding, language use, and unstructured human text. This project aims to use sentiment analysis techniques using different deep learning techniques. It will focus on binary sentiment classification, which detects the polarity in a text into 2 classes, positive and negative. This project studied different sentiment analysis techniques such as VADER,SVM, Naïve Bayes CNN,RNN, LSTM, GRU, and BERT. BERT gives the best accuracy among the available techniques but with the drawback that it takes a longer time to train.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE21-0288en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleTrue language understanding for an explainable AI systemen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLi Fangen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.organizationSingapore Management Universityen_US
dc.contributor.supervisor2Wang Zhaoxiaen_US
dc.contributor.supervisoremailASFLi@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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