Sentiment analysis based on deep learning
With the advent of the digital age, the ideas of our collective society have never been so easily accessible. Thousands of feelings and opinions are uploaded onto various social media platforms every minute of the day, whether they be in the form of a single sentence or a lengthy article. This...
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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/10356/158160 |
| _version_ | 1826124862203101184 |
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| author | Chan, Benjamin Wei Xun |
| author2 | Mao Kezhi |
| author_facet | Mao Kezhi Chan, Benjamin Wei Xun |
| author_sort | Chan, Benjamin Wei Xun |
| collection | NTU |
| description | With the advent of the digital age, the ideas of our collective society have never been
so easily accessible. Thousands of feelings and opinions are uploaded onto various
social media platforms every minute of the day, whether they be in the form of a
single sentence or a lengthy article. This results in institutions requiring techniques to
classify this deluge of data, allowing for the analysis of the sentiments of the
populace. Such techniques for the analysis of text data fall under the category of
Natural Language Processing (NLP), of which Sentiment Analysis is a part of.
Sentiment Analysis is the process in which the feelings of a writer of a piece of text
are analysed using machine learning. One of the ways the feelings are categorised is
into “positive”, “negative” and “neutral” tags, which can then be further analysed
using more conventional statistical methods.
This project aims to study the ways Sentiment Analysis can be improved based on
deep learning methods, focusing on the differences in accuracy between different
neural network models. These models include CNN, LSTM and BERT models, and
methods used to improve on their accuracy. |
| first_indexed | 2024-10-01T06:27:37Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/158160 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:27:37Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1581602023-07-07T19:23:32Z Sentiment analysis based on deep learning Chan, Benjamin Wei Xun Mao Kezhi School of Electrical and Electronic Engineering EKZMao@ntu.edu.sg Engineering::Electrical and electronic engineering With the advent of the digital age, the ideas of our collective society have never been so easily accessible. Thousands of feelings and opinions are uploaded onto various social media platforms every minute of the day, whether they be in the form of a single sentence or a lengthy article. This results in institutions requiring techniques to classify this deluge of data, allowing for the analysis of the sentiments of the populace. Such techniques for the analysis of text data fall under the category of Natural Language Processing (NLP), of which Sentiment Analysis is a part of. Sentiment Analysis is the process in which the feelings of a writer of a piece of text are analysed using machine learning. One of the ways the feelings are categorised is into “positive”, “negative” and “neutral” tags, which can then be further analysed using more conventional statistical methods. This project aims to study the ways Sentiment Analysis can be improved based on deep learning methods, focusing on the differences in accuracy between different neural network models. These models include CNN, LSTM and BERT models, and methods used to improve on their accuracy. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-27T07:23:58Z 2022-05-27T07:23:58Z 2022 Final Year Project (FYP) Chan, B. W. X. (2022). Sentiment analysis based on deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158160 https://hdl.handle.net/10356/158160 en A1096-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Chan, Benjamin Wei Xun Sentiment analysis based on deep learning |
| title | Sentiment analysis based on deep learning |
| title_full | Sentiment analysis based on deep learning |
| title_fullStr | Sentiment analysis based on deep learning |
| title_full_unstemmed | Sentiment analysis based on deep learning |
| title_short | Sentiment analysis based on deep learning |
| title_sort | sentiment analysis based on deep learning |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/158160 |
| work_keys_str_mv | AT chanbenjaminweixun sentimentanalysisbasedondeeplearning |