Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features

Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features

In this paper, a neural network is proposed to analyse Twitter sentiment classification for the Twitter domain. The study examines and evaluates the performance of neural networks with word embedding features in Twitter sentiment classification. Four benchmark datasets were used to represent differe...

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Bibliographic Details
Published in:Frontiers in Artificial Intelligence and Applications
Main Author: Zainuddin N.; Selamat A.; Ibrahim R.
Format: Conference paper
Language:English
Published: IOS Press BV 2018
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063377228&doi=10.3233%2f978-1-61499-900-3-723&partnerID=40&md5=06b19da482304287abdd18fe2e76983a
id 2-s2.0-85063377228
spelling 2-s2.0-85063377228
Zainuddin N.; Selamat A.; Ibrahim R.
Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
2018
Frontiers in Artificial Intelligence and Applications
303

10.3233/978-1-61499-900-3-723
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063377228&doi=10.3233%2f978-1-61499-900-3-723&partnerID=40&md5=06b19da482304287abdd18fe2e76983a
In this paper, a neural network is proposed to analyse Twitter sentiment classification for the Twitter domain. The study examines and evaluates the performance of neural networks with word embedding features in Twitter sentiment classification. Four benchmark datasets were used to represent different domains. The results indicated that the proposed method significantly improves the accuracy of the neural network classifier compared to existing works in aspect-based sentiment classification, especially for the highly imbalanced dataset. © 2018 The authors and IOS Press. All rights reserved.
IOS Press BV
9226389
English
Conference paper
author Zainuddin N.; Selamat A.; Ibrahim R.
spellingShingle Zainuddin N.; Selamat A.; Ibrahim R.
Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
author_facet Zainuddin N.; Selamat A.; Ibrahim R.
author_sort Zainuddin N.; Selamat A.; Ibrahim R.
title Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
title_short Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
title_full Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
title_fullStr Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
title_full_unstemmed Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
title_sort Evaluating aspect-based sentiment classification on Twitter hate speech using neural networks and word embedding features
publishDate 2018
container_title Frontiers in Artificial Intelligence and Applications
container_volume 303
container_issue
doi_str_mv 10.3233/978-1-61499-900-3-723
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063377228&doi=10.3233%2f978-1-61499-900-3-723&partnerID=40&md5=06b19da482304287abdd18fe2e76983a
description In this paper, a neural network is proposed to analyse Twitter sentiment classification for the Twitter domain. The study examines and evaluates the performance of neural networks with word embedding features in Twitter sentiment classification. Four benchmark datasets were used to represent different domains. The results indicated that the proposed method significantly improves the accuracy of the neural network classifier compared to existing works in aspect-based sentiment classification, especially for the highly imbalanced dataset. © 2018 The authors and IOS Press. All rights reserved.
publisher IOS Press BV
issn 9226389
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1809677605533646848

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