True language understanding for an explainable AI system
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 whic...
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| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/157406 |
| _version_ | 1811694997754347520 |
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| author | Farhan Khalifa Ibrahim |
| author2 | Li Fang |
| author_facet | Li Fang Farhan Khalifa Ibrahim |
| author_sort | Farhan Khalifa Ibrahim |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T07:16:28Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157406 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:16:28Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1574062022-05-14T13:01:33Z True language understanding for an explainable AI system Farhan Khalifa Ibrahim Li Fang School of Computer Science and Engineering Singapore Management University Wang Zhaoxia ASFLi@ntu.edu.sg Engineering::Computer science and engineering 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. Bachelor of Engineering (Computer Science) 2022-05-14T13:01:33Z 2022-05-14T13:01:33Z 2022 Final Year Project (FYP) 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 https://hdl.handle.net/10356/157406 en SCSE21-0288 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Farhan Khalifa Ibrahim True language understanding for an explainable AI system |
| title | True language understanding for an explainable AI system |
| title_full | True language understanding for an explainable AI system |
| title_fullStr | True language understanding for an explainable AI system |
| title_full_unstemmed | True language understanding for an explainable AI system |
| title_short | True language understanding for an explainable AI system |
| title_sort | true language understanding for an explainable ai system |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/157406 |
| work_keys_str_mv | AT farhankhalifaibrahim truelanguageunderstandingforanexplainableaisystem |