Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network

Zhengyuan Liu; Jin Cheon Na

doi:10.1007/978-3-030-04257-8_9

Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network

Zhengyuan Liu, Jin Cheon Na^*

^*Corresponding author for this work

Nanyang Technological University

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

7 Citations (Scopus)

Abstract

Opinion mining of social networking sites like Facebook and Twitter plays an important role in exploring valuable online user-generated contents. In contrast to sentence-level sentiment classification, the aspect-based analysis which can infer polarities towards various aspects in one sentence could obtain more in-depth insight. However, in traditional machine learning approaches, training such a fine-grained model often needs certain manual feature engineering. In this article, we proposed a deep learning model for aspect-level sentiment analysis and applied it to nuclear energy related tweets for understanding public opinions towards nuclear energy. We also built a new dataset for this task and the evaluation results showed that our attentive neural network could obtain insightful inference in rather complex expression forms and achieve state-of-the-art performance.

Original language	English
Title of host publication	Maturity and Innovation in Digital Libraries - 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Proceedings
Editors	Maja Žumer, Annika Hinze, Milena Dobreva
Publisher	Springer Verlag
Pages	99-111
Number of pages	13
ISBN (Print)	9783030042561
DOIs	https://doi.org/10.1007/978-3-030-04257-8_9
Publication status	Published - 2018
Externally published	Yes
Event	20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018 - Hamilton, New Zealand Duration: Nov 19 2018 → Nov 22 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11279 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018
Country/Territory	New Zealand
City	Hamilton
Period	11/19/18 → 11/22/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Deep learning
Natural language processing
Sentiment analysis
Social network

Access to Document

10.1007/978-3-030-04257-8_9

Cite this

Liu, Z., & Na, J. C. (2018). Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network. In M. Žumer, A. Hinze, & M. Dobreva (Eds.), Maturity and Innovation in Digital Libraries - 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Proceedings (pp. 99-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11279 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04257-8_9

Liu, Zhengyuan ; Na, Jin Cheon. / Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network. Maturity and Innovation in Digital Libraries - 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Proceedings. editor / Maja Žumer ; Annika Hinze ; Milena Dobreva. Springer Verlag, 2018. pp. 99-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Opinion mining of social networking sites like Facebook and Twitter plays an important role in exploring valuable online user-generated contents. In contrast to sentence-level sentiment classification, the aspect-based analysis which can infer polarities towards various aspects in one sentence could obtain more in-depth insight. However, in traditional machine learning approaches, training such a fine-grained model often needs certain manual feature engineering. In this article, we proposed a deep learning model for aspect-level sentiment analysis and applied it to nuclear energy related tweets for understanding public opinions towards nuclear energy. We also built a new dataset for this task and the evaluation results showed that our attentive neural network could obtain insightful inference in rather complex expression forms and achieve state-of-the-art performance.",

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Liu, Z & Na, JC 2018, Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network. in M Žumer, A Hinze & M Dobreva (eds), Maturity and Innovation in Digital Libraries - 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11279 LNCS, Springer Verlag, pp. 99-111, 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Hamilton, New Zealand, 11/19/18. https://doi.org/10.1007/978-3-030-04257-8_9

Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network. / Liu, Zhengyuan; Na, Jin Cheon.
Maturity and Innovation in Digital Libraries - 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Proceedings. ed. / Maja Žumer; Annika Hinze; Milena Dobreva. Springer Verlag, 2018. p. 99-111 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11279 LNCS).

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

TY - GEN

T1 - Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network

AU - Liu, Zhengyuan

AU - Na, Jin Cheon

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Opinion mining of social networking sites like Facebook and Twitter plays an important role in exploring valuable online user-generated contents. In contrast to sentence-level sentiment classification, the aspect-based analysis which can infer polarities towards various aspects in one sentence could obtain more in-depth insight. However, in traditional machine learning approaches, training such a fine-grained model often needs certain manual feature engineering. In this article, we proposed a deep learning model for aspect-level sentiment analysis and applied it to nuclear energy related tweets for understanding public opinions towards nuclear energy. We also built a new dataset for this task and the evaluation results showed that our attentive neural network could obtain insightful inference in rather complex expression forms and achieve state-of-the-art performance.

AB - Opinion mining of social networking sites like Facebook and Twitter plays an important role in exploring valuable online user-generated contents. In contrast to sentence-level sentiment classification, the aspect-based analysis which can infer polarities towards various aspects in one sentence could obtain more in-depth insight. However, in traditional machine learning approaches, training such a fine-grained model often needs certain manual feature engineering. In this article, we proposed a deep learning model for aspect-level sentiment analysis and applied it to nuclear energy related tweets for understanding public opinions towards nuclear energy. We also built a new dataset for this task and the evaluation results showed that our attentive neural network could obtain insightful inference in rather complex expression forms and achieve state-of-the-art performance.

KW - Deep learning

KW - Natural language processing

KW - Sentiment analysis

KW - Social network

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Maturity and Innovation in Digital Libraries - 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Proceedings

A2 - Žumer, Maja

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PB - Springer Verlag

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ER -

Liu Z, Na JC. Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network. In Žumer M, Hinze A, Dobreva M, editors, Maturity and Innovation in Digital Libraries - 20th International Conference on Asia-Pacific Digital Libraries, ICADL 2018, Proceedings. Springer Verlag. 2018. p. 99-111. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04257-8_9

Aspect-based sentiment analysis of nuclear energy tweets with attentive deep neural network

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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