Multi-channel convolutional neural network for targeted sentiment classification

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)
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:	Organisation
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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