Open-domain sentiment analysis

The proliferation of social media on the Internet in recent years has led to an increased amount of user-generated information worldwide. These platforms have allowed people of different backgrounds to share their opinions regarding the news and events occurring around them. Research regarding senti...

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Main Author: Tong, Zi Hang
Other Authors: Pan Jialin, Sinno
Format: Final Year Project
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/70529
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-705292023-03-03T20:30:37Z Open-domain sentiment analysis Tong, Zi Hang Pan Jialin, Sinno School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing The proliferation of social media on the Internet in recent years has led to an increased amount of user-generated information worldwide. These platforms have allowed people of different backgrounds to share their opinions regarding the news and events occurring around them. Research regarding sentiment analysis had always been conducted within a closed domain, under the assumption of a static vocabulary. However, new words or phrases constantly appear in social media under various contexts. Thus, the sentiment lexicon built offline from the training data may be inaccurate when being used to make predictions. The proposed framework can extend a sentiment lexicon for Twitter based on incoming tweets dynamically. Results of the experiment has proven that expanding the sentiment lexicon dynamically does improve the accuracy and precision of the sentiment classifier, although the efficacy of the classifier is affected due to the added load. Bachelor of Engineering (Computer Science) 2017-04-26T07:30:27Z 2017-04-26T07:30:27Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70529 en Nanyang Technological University 41 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing
spellingShingle DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Tong, Zi Hang
Open-domain sentiment analysis
description The proliferation of social media on the Internet in recent years has led to an increased amount of user-generated information worldwide. These platforms have allowed people of different backgrounds to share their opinions regarding the news and events occurring around them. Research regarding sentiment analysis had always been conducted within a closed domain, under the assumption of a static vocabulary. However, new words or phrases constantly appear in social media under various contexts. Thus, the sentiment lexicon built offline from the training data may be inaccurate when being used to make predictions. The proposed framework can extend a sentiment lexicon for Twitter based on incoming tweets dynamically. Results of the experiment has proven that expanding the sentiment lexicon dynamically does improve the accuracy and precision of the sentiment classifier, although the efficacy of the classifier is affected due to the added load.
author2 Pan Jialin, Sinno
author_facet Pan Jialin, Sinno
Tong, Zi Hang
format Final Year Project
author Tong, Zi Hang
author_sort Tong, Zi Hang
title Open-domain sentiment analysis
title_short Open-domain sentiment analysis
title_full Open-domain sentiment analysis
title_fullStr Open-domain sentiment analysis
title_full_unstemmed Open-domain sentiment analysis
title_sort open-domain sentiment analysis
publishDate 2017
url http://hdl.handle.net/10356/70529
_version_ 1759856905908387840