Sentiment analysis on student well-being in Singapore
The issue of well-being among students, particularly at the university level, has become increasingly important in recent years due to the growing awareness of mental health concerns among students. This project aims to analyze the sentiments and well-being of university students in Singapore by uti...
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Nanyang Technological University
2024
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sg-ntu-dr.10356-1753542024-04-26T15:43:21Z Sentiment analysis on student well-being in Singapore Lim, Chien Hui Erik Cambria School of Computer Science and Engineering cambria@ntu.edu.sg Computer and Information Science The issue of well-being among students, particularly at the university level, has become increasingly important in recent years due to the growing awareness of mental health concerns among students. This project aims to analyze the sentiments and well-being of university students in Singapore by utilizing sentiment analysis tools and models on social media text. By examining general trends in sentiments, schools can identify areas that require attention and develop targeted solutions to address specific factors that contribute to poor well-being among students. Various sentiment analysis models were applied, including VADER, RoBERTa, and SenticNet, to classify polarity, while BART and SenticNet were used to classify well-being. The results indicated that RoBERTa had the highest accuracy rate of 77% for detecting polarity, while BART had the highest accuracy rate of 73% for detecting well-being. Bachelor's degree 2024-04-22T06:21:48Z 2024-04-22T06:21:48Z 2024 Final Year Project (FYP) Lim, C. H. (2024). Sentiment analysis on student well-being in Singapore. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175354 https://hdl.handle.net/10356/175354 en application/pdf Nanyang Technological University |
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Computer and Information Science Lim, Chien Hui Sentiment analysis on student well-being in Singapore |
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The issue of well-being among students, particularly at the university level, has become increasingly important in recent years due to the growing awareness of mental health concerns among students. This project aims to analyze the sentiments and well-being of university students in Singapore by utilizing sentiment analysis tools and models on social media text. By examining general trends in sentiments, schools can identify areas that require attention and develop targeted solutions to address specific factors that contribute to poor well-being among students.
Various sentiment analysis models were applied, including VADER, RoBERTa, and SenticNet, to classify polarity, while BART and SenticNet were used to classify well-being. The results indicated that RoBERTa had the highest accuracy rate of 77% for detecting polarity, while BART had the highest accuracy rate of 73% for detecting well-being. |
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Erik Cambria |
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Erik Cambria Lim, Chien Hui |
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Final Year Project |
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Lim, Chien Hui |
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Lim, Chien Hui |
title |
Sentiment analysis on student well-being in Singapore |
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Sentiment analysis on student well-being in Singapore |
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Sentiment analysis on student well-being in Singapore |
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Sentiment analysis on student well-being in Singapore |
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Sentiment analysis on student well-being in Singapore |
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sentiment analysis on student well-being in singapore |
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Nanyang Technological University |
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2024 |
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https://hdl.handle.net/10356/175354 |
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