Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157406
Title: True language understanding for an explainable AI system
Authors: Farhan Khalifa Ibrahim
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Farhan Khalifa Ibrahim (2022). True language understanding for an explainable AI system. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157406
Project: SCSE21-0288
Abstract: Millions 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.
URI: https://hdl.handle.net/10356/157406
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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