Yuan, Ting, Li, Haihui, Zhao, Hongya, Cai, Qianhua, Liu, Han ORCID: https://orcid.org/0000-0002-7731-8258 and Hu, Xiaohui 2019. Multi-channel convolutional neural network for targeted sentiment classification. Presented at: International Conference on Machine Learning and Cybernetics, Kobe, Japan, 7-10 July 2019. 2019 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 10.1109/ICMLC48188.2019.8949286 |
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Abstract
In recent years, targeted sentiment analysis has received great attention as a fine-grained sentiment analysis. Determining the sentiment polarity of a specific target in a sentence is the main task. This paper proposes a multi-channel convolutional neural network (MCL-CNN) for targeted sentiment classification. Our approach can not only parallelize over the words of a sentence, but also extract local features effectively. Contexts and targets can be more comprehensively utilized by using part-of-speech information, semantic information and interactive information, so that diverse features can be obtained. Finally, experimental results on the SemEval 2014 dataset demonstrate the effectiveness of this method.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Publication |
Status: | Published |
Schools: | Computer Science & Informatics |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Publisher: | IEEE |
ISBN: | 9781728128177 |
ISSN: | 2160-133X |
Related URLs: | |
Date of First Compliant Deposit: | 19 July 2019 |
Date of Acceptance: | 21 May 2019 |
Last Modified: | 07 Dec 2022 13:56 |
URI: | https://orca.cardiff.ac.uk/id/eprint/124286 |
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