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: | 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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