Enhancing sentiment analysis for social media data

Social media platforms are now widely used by people to express their feeling and opinions on various current issues and popular topics. The study of sentiment analysis of social media data benefits in evaluating opinions and gaining insights for researchers and business decision makers. This projec...

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Bibliographic Details
Main Author:	Xu, Qianqian
Other Authors:	Li Fang
Format:	Final Year Project
Language:	English
Published:	2017
Subjects:	DRNTU::Engineering::Computer science and engineering
Online Access:	http://hdl.handle.net/10356/70509
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-70509
record_format	dspace
spelling	sg-ntu-dr.10356-705092023-03-03T20:30:33Z Enhancing sentiment analysis for social media data Xu, Qianqian Li Fang School of Computer Science and Engineering A*STAR DRNTU::Engineering::Computer science and engineering Social media platforms are now widely used by people to express their feeling and opinions on various current issues and popular topics. The study of sentiment analysis of social media data benefits in evaluating opinions and gaining insights for researchers and business decision makers. This project explores the enhancement techniques in machine-learning based sentiment analysis for tweets. Data pre-processing, negation handling, feature extractor, one-step and two-step classification processes are implemented in this project to enhance the performance of classifiers. The results indicate that different enhancement techniques can improve classification accuracy differently. Bachelor of Engineering (Computer Science) 2017-04-26T03:36:03Z 2017-04-26T03:36:03Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70509 en Nanyang Technological University 37 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
spellingShingle	DRNTU::Engineering::Computer science and engineering Xu, Qianqian Enhancing sentiment analysis for social media data
description	Social media platforms are now widely used by people to express their feeling and opinions on various current issues and popular topics. The study of sentiment analysis of social media data benefits in evaluating opinions and gaining insights for researchers and business decision makers. This project explores the enhancement techniques in machine-learning based sentiment analysis for tweets. Data pre-processing, negation handling, feature extractor, one-step and two-step classification processes are implemented in this project to enhance the performance of classifiers. The results indicate that different enhancement techniques can improve classification accuracy differently.
author2	Li Fang
author_facet	Li Fang Xu, Qianqian
format	Final Year Project
author	Xu, Qianqian
author_sort	Xu, Qianqian
title	Enhancing sentiment analysis for social media data
title_short	Enhancing sentiment analysis for social media data
title_full	Enhancing sentiment analysis for social media data
title_fullStr	Enhancing sentiment analysis for social media data
title_full_unstemmed	Enhancing sentiment analysis for social media data
title_sort	enhancing sentiment analysis for social media data
publishDate	2017
url	http://hdl.handle.net/10356/70509
_version_	1759857654651420672

Enhancing sentiment analysis for social media data

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