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Overview and future opportunities of sentiment analysis approaches for big data

The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growt...

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Main Authors: Mohd Sharef, Nurfadhlina, Mat Zin, Harnani, Nadali, Samaneh
Format: Article
Language:English
Published: Science Publications 2016
Online Access:http://psasir.upm.edu.my/id/eprint/35372/1/jcssp.2016.153.168.pdf
http://psasir.upm.edu.my/id/eprint/35372/
http://thescipub.com/abstract/10.3844/jcssp.2016.153.168
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spelling my.upm.eprints.353722016-10-12T08:19:46Z http://psasir.upm.edu.my/id/eprint/35372/ Overview and future opportunities of sentiment analysis approaches for big data Mohd Sharef, Nurfadhlina Mat Zin, Harnani Nadali, Samaneh The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common for businesses. However, scarce works have studied the gaps of SA application in big data. The contribution of this paper is two-fold: (i) this study reviews the state of the art of SA approaches. including sentiment polarity detection, SA features (explicit and implicit), sentiment classification techniques and applications of SA and (ii) this study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored. SA studies are predicted to be expanded into approaches that utilise scalability, possess high adaptability for source variation, velocity and veracity to maximise value mining for the benefit of the users. Science Publications 2016 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/35372/1/jcssp.2016.153.168.pdf Mohd Sharef, Nurfadhlina and Mat Zin, Harnani and Nadali, Samaneh (2016) Overview and future opportunities of sentiment analysis approaches for big data. Journal of Computer Science, 12 (3). pp. 153-168. ISSN 1549-3636; ESSN: 1552-6607 http://thescipub.com/abstract/10.3844/jcssp.2016.153.168 10.3844/jcssp.2016.153.168
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The ability to exploit public sentiment in social media is increasingly considered as an important tool for market understanding, customer segmentation and stock price prediction for strategic marketing planning and manoeuvring. This evolution of technology adoption is energised by the healthy growth in big data framework, which caused applications based on Sentiment Analysis (SA) in big data to become common for businesses. However, scarce works have studied the gaps of SA application in big data. The contribution of this paper is two-fold: (i) this study reviews the state of the art of SA approaches. including sentiment polarity detection, SA features (explicit and implicit), sentiment classification techniques and applications of SA and (ii) this study reviews the suitability of SA approaches for application in the big data frameworks, as well as highlights the gaps and suggests future works that should be explored. SA studies are predicted to be expanded into approaches that utilise scalability, possess high adaptability for source variation, velocity and veracity to maximise value mining for the benefit of the users.
format Article
author Mohd Sharef, Nurfadhlina
Mat Zin, Harnani
Nadali, Samaneh
spellingShingle Mohd Sharef, Nurfadhlina
Mat Zin, Harnani
Nadali, Samaneh
Overview and future opportunities of sentiment analysis approaches for big data
author_facet Mohd Sharef, Nurfadhlina
Mat Zin, Harnani
Nadali, Samaneh
author_sort Mohd Sharef, Nurfadhlina
title Overview and future opportunities of sentiment analysis approaches for big data
title_short Overview and future opportunities of sentiment analysis approaches for big data
title_full Overview and future opportunities of sentiment analysis approaches for big data
title_fullStr Overview and future opportunities of sentiment analysis approaches for big data
title_full_unstemmed Overview and future opportunities of sentiment analysis approaches for big data
title_sort overview and future opportunities of sentiment analysis approaches for big data
publisher Science Publications
publishDate 2016
url http://psasir.upm.edu.my/id/eprint/35372/1/jcssp.2016.153.168.pdf
http://psasir.upm.edu.my/id/eprint/35372/
http://thescipub.com/abstract/10.3844/jcssp.2016.153.168
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score 13.211869

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