A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets

Joao Paulo Aires; Carlos Padilha; Christian Quevedo; Felipe Meneguzzi

doi:10.1109/IJCNN.2018.8489760

A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets

Joao Paulo Aires, Carlos Padilha, Christian Quevedo, Felipe Meneguzzi

Computing Science

Pontifícia Universidade Católica do Rio Grande do Sul

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

9 Citations (Scopus)

Abstract

Sentiment analysis is an important technique to interpret user opinion on products from text, for example, as shared in social media. Recent approaches using deep learning can accurately extract overall sentiment from large datasets. However, extracting sentiment from specific aspects of a product with small training datasets remains a challenge. The automatic classification of sentiments at aspect-level can provide more detailed feedbacks about product and service opinions avoiding manual verification. In this work, we develop two deep learning approaches to classify sentiment at aspect-level using small datasets.

Original language	English
Title of host publication	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060146
DOIs	https://doi.org/10.1109/IJCNN.2018.8489760
Publication status	Published - 10 Oct 2018
Event	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2018-July

Conference

Conference	2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Bibliographical note

Funding Information:
The authors would like to acknowledge Motorola Mobility for funding this research. Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1.

Publisher Copyright:
© 2018 IEEE.

Keywords

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Access to Document

10.1109/IJCNN.2018.8489760

Cite this

Aires, J. P., Padilha, C., Quevedo, C., & Meneguzzi, F. (2018). A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Article 8489760 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489760

A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets. / Aires, Joao Paulo; Padilha, Carlos; Quevedo, Christian et al.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8489760 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Aires, JP, Padilha, C, Quevedo, C & Meneguzzi, F 2018, A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets. in 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings., 8489760, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/IJCNN.2018.8489760

Aires JP, Padilha C, Quevedo C, Meneguzzi F. A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8489760. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2018.8489760

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A Deep Learning Approach to Classify Aspect-Level Sentiment using Small Datasets

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