Sentiment analysis based on NLP and deep learning
Sentiment analysis is a subfield of natural language processing that extracts and identifies sentiments from a string of text. They can be carried out by deep learning models such as RNN, CNN, LSTM, Bi-LSTM and transformer-based models such as BERT, DistilBERT, RoBERTa, XLNET and GPT. This project r...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167544 |
| _version_ | 1826113767068401664 |
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| author | Lim, Zion Ziheng |
| author2 | Mao Kezhi |
| author_facet | Mao Kezhi Lim, Zion Ziheng |
| author_sort | Lim, Zion Ziheng |
| collection | NTU |
| description | Sentiment analysis is a subfield of natural language processing that extracts and identifies sentiments from a string of text. They can be carried out by deep learning models such as RNN, CNN, LSTM, Bi-LSTM and transformer-based models such as BERT, DistilBERT, RoBERTa, XLNET and GPT. This project reviews recent advances in deep learning models for sentiment analysis on datasets that are publicly available. The datasets chosen are from twitter, IMDB, SST2, Yelp and Amazon. We also highlight some of the factors that could affect the performance of the deep learning models such as text representation techniques and hyperparameters. The text representation techniques reviewed are BOW, Word2Vec, GloVe and FastText. Hyperparameters are fine-tuned on the transformer models and their effects can be studied from the results obtained. |
| first_indexed | 2024-10-01T03:28:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167544 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:28:37Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1675442023-07-07T15:53:40Z Sentiment analysis based on NLP and deep learning Lim, Zion Ziheng Mao Kezhi School of Electrical and Electronic Engineering EKZMao@ntu.edu.sg Engineering::Electrical and electronic engineering Sentiment analysis is a subfield of natural language processing that extracts and identifies sentiments from a string of text. They can be carried out by deep learning models such as RNN, CNN, LSTM, Bi-LSTM and transformer-based models such as BERT, DistilBERT, RoBERTa, XLNET and GPT. This project reviews recent advances in deep learning models for sentiment analysis on datasets that are publicly available. The datasets chosen are from twitter, IMDB, SST2, Yelp and Amazon. We also highlight some of the factors that could affect the performance of the deep learning models such as text representation techniques and hyperparameters. The text representation techniques reviewed are BOW, Word2Vec, GloVe and FastText. Hyperparameters are fine-tuned on the transformer models and their effects can be studied from the results obtained. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-29T05:39:06Z 2023-05-29T05:39:06Z 2023 Final Year Project (FYP) Lim, Z. Z. (2023). Sentiment analysis based on NLP and deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167544 https://hdl.handle.net/10356/167544 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Lim, Zion Ziheng Sentiment analysis based on NLP and deep learning |
| title | Sentiment analysis based on NLP and deep learning |
| title_full | Sentiment analysis based on NLP and deep learning |
| title_fullStr | Sentiment analysis based on NLP and deep learning |
| title_full_unstemmed | Sentiment analysis based on NLP and deep learning |
| title_short | Sentiment analysis based on NLP and deep learning |
| title_sort | sentiment analysis based on nlp and deep learning |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/167544 |
| work_keys_str_mv | AT limzionziheng sentimentanalysisbasedonnlpanddeeplearning |