Sentiment analysis technique: A look into support vector machine and naive bayes

Sentiment Analysis and opinion mining aims to analyze sentiments, opinions, emotions etc. towards products, services or current topics. There are various approaches applied to mine the sentiments portrayed. Supervised machine learning is one such approach that is generally applied. The aim of this...

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Main Authors:	Kaur, W., Vimala, B.
Format:	Conference or Workshop Item
Language:	English
Published:	2016
Subjects:	T Technology (General) TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/15535/1/0001.pdf http://eprints.um.edu.my/15535/
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Institution:	Universiti Malaya
Language:	English

id	my.um.eprints.15535
record_format	eprints
spelling	my.um.eprints.155352016-01-20T04:22:33Z http://eprints.um.edu.my/15535/ Sentiment analysis technique: A look into support vector machine and naive bayes Kaur, W. Vimala, B. T Technology (General) TA Engineering (General). Civil engineering (General) Sentiment Analysis and opinion mining aims to analyze sentiments, opinions, emotions etc. towards products, services or current topics. There are various approaches applied to mine the sentiments portrayed. Supervised machine learning is one such approach that is generally applied. The aim of this paper is to investigate the current methods used to perform sentiment analysis by reviewing and comparing recently published research. The findings are discussed in hope that it would help future researchers to gain an understanding of a possible method they could adopt or even come up with a new approach to better mine sentiments from big data that is tailored to suit the need of their data source. 2016-01 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.um.edu.my/15535/1/0001.pdf Kaur, W. and Vimala, B. (2016) Sentiment analysis technique: A look into support vector machine and naive bayes. In: International Conference on IT, Mechanical & Communication Engineering (ICIME 2016), 02-03 January 2016, Pattaya, Thailand.
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
language	English
topic	T Technology (General) TA Engineering (General). Civil engineering (General)
spellingShingle	T Technology (General) TA Engineering (General). Civil engineering (General) Kaur, W. Vimala, B. Sentiment analysis technique: A look into support vector machine and naive bayes
description	Sentiment Analysis and opinion mining aims to analyze sentiments, opinions, emotions etc. towards products, services or current topics. There are various approaches applied to mine the sentiments portrayed. Supervised machine learning is one such approach that is generally applied. The aim of this paper is to investigate the current methods used to perform sentiment analysis by reviewing and comparing recently published research. The findings are discussed in hope that it would help future researchers to gain an understanding of a possible method they could adopt or even come up with a new approach to better mine sentiments from big data that is tailored to suit the need of their data source.
format	Conference or Workshop Item
author	Kaur, W. Vimala, B.
author_facet	Kaur, W. Vimala, B.
author_sort	Kaur, W.
title	Sentiment analysis technique: A look into support vector machine and naive bayes
title_short	Sentiment analysis technique: A look into support vector machine and naive bayes
title_full	Sentiment analysis technique: A look into support vector machine and naive bayes
title_fullStr	Sentiment analysis technique: A look into support vector machine and naive bayes
title_full_unstemmed	Sentiment analysis technique: A look into support vector machine and naive bayes
title_sort	sentiment analysis technique: a look into support vector machine and naive bayes
publishDate	2016
url	http://eprints.um.edu.my/15535/1/0001.pdf http://eprints.um.edu.my/15535/
_version_	1643690076139421696

Sentiment analysis technique: A look into support vector machine and naive bayes

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