Review sentiment analysis based on deep neural network
With the rapid development of the Internet and related technologies, network data has shown a spurt of growth, mainly in the form of text. With this growth trend of data, text classification has become an increasingly important research topic. The use of deep learning technology to represent text ha...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/143417 |
| _version_ | 1811678951441956864 |
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| author | Song, Wanzhen |
| author2 | Mao Kezhi |
| author_facet | Mao Kezhi Song, Wanzhen |
| author_sort | Song, Wanzhen |
| collection | NTU |
| description | With the rapid development of the Internet and related technologies, network data has shown a spurt of growth, mainly in the form of text. With this growth trend of data, text classification has become an increasingly important research topic. The use of deep learning technology to represent text has received great attention from researchers. Based on this, this paper studies and implements the sentiment classification problem of comments. By investigating the research status of sentiment analysis, combined with the current cutting-edge technologies in the field of machine learning and deep learning, the five deep learning models are explored and compared. The goal is to understand the principles of the models and choose more suitable ones in different situations to improve the accuracy of sentiment classification. The work done in this dissertation mainly includes the following parts:
1) Build CNN, BiLSTM, BiLSTM-At, RCNN, Transformer with the concept of attention mechanism. Compare the effects of different models on eight different types of datasets.
2) To improve the effectiveness of the model, this dissertation sets comparative experiments on parameters selection, to determine parameters that affect the model performance, and explain how they affect the model performance.
3) An in-depth exploration of the principle of different models. Find out the weight of each word in determining the sentiment of the text. Visualize three classic models. Behind the model that determine the sentiment of the text.
4) According to 3) draw the advantages and disadvantages of different models and determine the suitable condition for different models. |
| first_indexed | 2024-10-01T03:01:25Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/143417 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:01:25Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1434172023-07-04T16:38:07Z Review sentiment analysis based on deep neural network Song, Wanzhen Mao Kezhi School of Electrical and Electronic Engineering EKZMao@ntu.edu.sg Engineering::Electrical and electronic engineering With the rapid development of the Internet and related technologies, network data has shown a spurt of growth, mainly in the form of text. With this growth trend of data, text classification has become an increasingly important research topic. The use of deep learning technology to represent text has received great attention from researchers. Based on this, this paper studies and implements the sentiment classification problem of comments. By investigating the research status of sentiment analysis, combined with the current cutting-edge technologies in the field of machine learning and deep learning, the five deep learning models are explored and compared. The goal is to understand the principles of the models and choose more suitable ones in different situations to improve the accuracy of sentiment classification. The work done in this dissertation mainly includes the following parts: 1) Build CNN, BiLSTM, BiLSTM-At, RCNN, Transformer with the concept of attention mechanism. Compare the effects of different models on eight different types of datasets. 2) To improve the effectiveness of the model, this dissertation sets comparative experiments on parameters selection, to determine parameters that affect the model performance, and explain how they affect the model performance. 3) An in-depth exploration of the principle of different models. Find out the weight of each word in determining the sentiment of the text. Visualize three classic models. Behind the model that determine the sentiment of the text. 4) According to 3) draw the advantages and disadvantages of different models and determine the suitable condition for different models. Master of Science (Electronics) 2020-08-31T06:13:13Z 2020-08-31T06:13:13Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/143417 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Song, Wanzhen Review sentiment analysis based on deep neural network |
| title | Review sentiment analysis based on deep neural network |
| title_full | Review sentiment analysis based on deep neural network |
| title_fullStr | Review sentiment analysis based on deep neural network |
| title_full_unstemmed | Review sentiment analysis based on deep neural network |
| title_short | Review sentiment analysis based on deep neural network |
| title_sort | review sentiment analysis based on deep neural network |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/143417 |
| work_keys_str_mv | AT songwanzhen reviewsentimentanalysisbasedondeepneuralnetwork |