Sentiment analysis based on deep neural networks

Now, with the rapid development of social media networks like Google, wikis, blogs, online forums, Twitter communities, Facebook communities, YouTube video platforms, and Tiktok short video platforms, the number and frequency of interpersonal read-write access interactions is increasing. Compared wi...

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Main Author: Zhang, Jingsheng
Other Authors: Mao Kezhi
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/152890
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1528902023-07-04T17:11:49Z Sentiment analysis based on deep neural networks Zhang, Jingsheng Mao Kezhi School of Electrical and Electronic Engineering EKZMao@ntu.edu.sg Engineering::Electrical and electronic engineering Now, with the rapid development of social media networks like Google, wikis, blogs, online forums, Twitter communities, Facebook communities, YouTube video platforms, and Tiktok short video platforms, the number and frequency of interpersonal read-write access interactions is increasing. Compared with existing methods, efficient machine learning models can perform accurate sentiment analysis on sample data. These models have many advantages, such as scalability, excellent real-time analysis capabilities, and consistent standards. Inspired by this, the main research content of this dissertation is sentiment analysis based on deep neural networks. This dissertation summarizes the sentiment analysis model based on deep neural networks. Develop deep neural network learning algorithms for sentiment analysis problems. The first part is Sentiment Analysis for Publicly Available Database. According to the existing database on the Internet, according to the results of various deep neural network models, compare the advantages and disadvantages of different classifiers, and observe and analyze the influence of different parameters on the model effect. According to different factors such as accuracy, variance, P value, F1 score and training time, the models are compared and improved. The second part is Sentiment Analysis on policy study. This section focuses on Singapore’s comments on the "tourism bubble" policy on social networking sites. For this specific policy, collect comments from people on the Internet and develop a deep neural network algorithm for sentiment analysis. Master of Science (Computer Control and Automation) 2021-10-13T01:24:06Z 2021-10-13T01:24:06Z 2021 Thesis-Master by Coursework Zhang, J. (2021). Sentiment analysis based on deep neural networks. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/152890 https://hdl.handle.net/10356/152890 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Zhang, Jingsheng
Sentiment analysis based on deep neural networks
description Now, with the rapid development of social media networks like Google, wikis, blogs, online forums, Twitter communities, Facebook communities, YouTube video platforms, and Tiktok short video platforms, the number and frequency of interpersonal read-write access interactions is increasing. Compared with existing methods, efficient machine learning models can perform accurate sentiment analysis on sample data. These models have many advantages, such as scalability, excellent real-time analysis capabilities, and consistent standards. Inspired by this, the main research content of this dissertation is sentiment analysis based on deep neural networks. This dissertation summarizes the sentiment analysis model based on deep neural networks. Develop deep neural network learning algorithms for sentiment analysis problems. The first part is Sentiment Analysis for Publicly Available Database. According to the existing database on the Internet, according to the results of various deep neural network models, compare the advantages and disadvantages of different classifiers, and observe and analyze the influence of different parameters on the model effect. According to different factors such as accuracy, variance, P value, F1 score and training time, the models are compared and improved. The second part is Sentiment Analysis on policy study. This section focuses on Singapore’s comments on the "tourism bubble" policy on social networking sites. For this specific policy, collect comments from people on the Internet and develop a deep neural network algorithm for sentiment analysis.
author2 Mao Kezhi
author_facet Mao Kezhi
Zhang, Jingsheng
format Thesis-Master by Coursework
author Zhang, Jingsheng
author_sort Zhang, Jingsheng
title Sentiment analysis based on deep neural networks
title_short Sentiment analysis based on deep neural networks
title_full Sentiment analysis based on deep neural networks
title_fullStr Sentiment analysis based on deep neural networks
title_full_unstemmed Sentiment analysis based on deep neural networks
title_sort sentiment analysis based on deep neural networks
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/152890
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