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...
| Published in: | Frontiers in Artificial Intelligence and Applications |
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| Main Author: | |
| Format: | Conference paper |
| Language: | English |
| Published: |
IOS Press BV
2018
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| 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 |
| Summary: | 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. |
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| ISSN: | 9226389 |
| DOI: | 10.3233/978-1-61499-900-3-723 |