Visualizing emotions in social conversations
Social media comments are a valuable resource for identifying sentiments and opinions that are posted by commenters across geographical and socio-economic backgrounds. In this report we illustrate how we apply sentiment analysis on YouTube comments with the help of our existing KANDINSKY Mobile appl...
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2022
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sg-ntu-dr.10356-1575462022-05-19T12:46:51Z Visualizing emotions in social conversations Bhadra, Soham Sourav S Bhowmick School of Computer Science and Engineering ASSourav@ntu.edu.sg Engineering::Computer science and engineering Social media comments are a valuable resource for identifying sentiments and opinions that are posted by commenters across geographical and socio-economic backgrounds. In this report we illustrate how we apply sentiment analysis on YouTube comments with the help of our existing KANDINSKY Mobile application, which provides a novel visualisation of YouTube comments, grouping related comments together as concentric circles in a virtual space. We first examine the structure of the application, followed by a detailed exploration into various popular sentiment analysis tools, particularly Sentiwordnet, SenticNet and VADER, which we then use to identify the polarity of sentiments contained in comments on YouTube videos. This is dependent on not only the words used in the sentence but also on various non-alphabetical factors such as emojis, foreign characters and exaggerated spellings that are common in social media comments. Finally we visually depict the polarities of these sentiments in the application. We hope that this project will be of great interest to academic researchers and the general public alike, as the identification and visualisation of the various types of sentiments is of significant relevance to everyday social conversations. Bachelor of Engineering (Computer Science) 2022-05-19T12:46:51Z 2022-05-19T12:46:51Z 2022 Final Year Project (FYP) Bhadra, S. (2022). Visualizing emotions in social conversations. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157546 https://hdl.handle.net/10356/157546 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Bhadra, Soham Visualizing emotions in social conversations |
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Social media comments are a valuable resource for identifying sentiments and opinions that are posted by commenters across geographical and socio-economic backgrounds. In this report we illustrate how we apply sentiment analysis on YouTube comments with the help of our existing KANDINSKY Mobile application, which provides a novel visualisation of YouTube comments, grouping related comments together as concentric circles in a virtual space. We first examine the structure of the application, followed by a detailed exploration into various popular sentiment analysis tools, particularly Sentiwordnet, SenticNet and VADER, which we then use to identify the polarity of sentiments contained in comments on YouTube videos. This is dependent on not only the words used in the sentence but also on various non-alphabetical factors such as emojis, foreign characters and exaggerated spellings that are common in social media comments. Finally we visually depict the polarities of these sentiments in the application. We hope that this project will be of great interest to academic researchers and the general public alike, as the identification and visualisation of the various types of sentiments is of significant relevance to everyday social conversations. |
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Sourav S Bhowmick |
author_facet |
Sourav S Bhowmick Bhadra, Soham |
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Final Year Project |
author |
Bhadra, Soham |
author_sort |
Bhadra, Soham |
title |
Visualizing emotions in social conversations |
title_short |
Visualizing emotions in social conversations |
title_full |
Visualizing emotions in social conversations |
title_fullStr |
Visualizing emotions in social conversations |
title_full_unstemmed |
Visualizing emotions in social conversations |
title_sort |
visualizing emotions in social conversations |
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Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/157546 |
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1734310271853264896 |