A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning

Snehameena Arumugam; Likai Peng; Jin Cheon Na; Guangze Lin; Roopika Ganesh; Xiaoyin Li; Qing Chen; Shirley S. Ho; Erik Cambria

doi:10.1007/978-3-030-91669-5_36

A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning

Snehameena Arumugam, Likai Peng, Jin Cheon Na^*, Guangze Lin, Roopika Ganesh, Xiaoyin Li, Qing Chen, Shirley S. Ho, Erik Cambria

^*Corresponding author for this work

Nanyang Technological University

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

2 Citations (Scopus)

Abstract

Nuclear energy is one of controversial topics that affects people’s lives, and it is important for policy makers to analyze what people feel towards the subject. But manual analysis of related user-generated contents on social media platforms is a daunting task, and automatic data analysis and visualization come to help. So, in this research, we firstly developed a model for classifying the emotion of nuclear energy related tweets and another model for the aspect-based sentiment analysis of nuclear energy tweets using the BERT (Bidirectional Encoder Representations from Transformers). After that, we developed a prototype system for visualization of the analyzed results stored in the database. The user interface dashboards of the system allow users to monitor emotion and sentiment trends towards nuclear energy by analyzing recent nuclear energy tweets crawled weekly.

Original language	English
Title of host publication	Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings
Editors	Hao-Ren Ke, Chei Sian Lee, Kazunari Sugiyama
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	471-479
Number of pages	9
ISBN (Print)	9783030916688
DOIs	https://doi.org/10.1007/978-3-030-91669-5_36
Publication status	Published - 2021
Externally published	Yes
Event	23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021 - Virtual, Online Duration: Dec 1 2021 → Dec 3 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13133 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021
City	Virtual, Online
Period	12/1/21 → 12/3/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Aspect based sentiment classification
BERT
Deep learning
Emotion analysis
Nuclear energy

Access to Document

10.1007/978-3-030-91669-5_36

Cite this

Arumugam, S., Peng, L., Na, J. C., Lin, G., Ganesh, R., Li, X., Chen, Q., Ho, S. S., & Cambria, E. (2021). A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning. In H.-R. Ke, C. S. Lee, & K. Sugiyama (Eds.), Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings (pp. 471-479). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13133 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-91669-5_36

Arumugam, Snehameena ; Peng, Likai ; Na, Jin Cheon et al. / A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning. Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings. editor / Hao-Ren Ke ; Chei Sian Lee ; Kazunari Sugiyama. Springer Science and Business Media Deutschland GmbH, 2021. pp. 471-479 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c6c8a4aa53774e168140b6284c5cf954,

title = "A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning",

abstract = "Nuclear energy is one of controversial topics that affects people{\textquoteright}s lives, and it is important for policy makers to analyze what people feel towards the subject. But manual analysis of related user-generated contents on social media platforms is a daunting task, and automatic data analysis and visualization come to help. So, in this research, we firstly developed a model for classifying the emotion of nuclear energy related tweets and another model for the aspect-based sentiment analysis of nuclear energy tweets using the BERT (Bidirectional Encoder Representations from Transformers). After that, we developed a prototype system for visualization of the analyzed results stored in the database. The user interface dashboards of the system allow users to monitor emotion and sentiment trends towards nuclear energy by analyzing recent nuclear energy tweets crawled weekly.",

keywords = "Aspect based sentiment classification, BERT, Deep learning, Emotion analysis, Nuclear energy",

author = "Snehameena Arumugam and Likai Peng and Na, {Jin Cheon} and Guangze Lin and Roopika Ganesh and Xiaoyin Li and Qing Chen and Ho, {Shirley S.} and Erik Cambria",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021 ; Conference date: 01-12-2021 Through 03-12-2021",

year = "2021",

doi = "10.1007/978-3-030-91669-5_36",

language = "English",

isbn = "9783030916688",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "471--479",

editor = "Hao-Ren Ke and Lee, {Chei Sian} and Kazunari Sugiyama",

booktitle = "Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings",

address = "Germany",

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Arumugam, S, Peng, L, Na, JC, Lin, G, Ganesh, R, Li, X, Chen, Q, Ho, SS & Cambria, E 2021, A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning. in H-R Ke, CS Lee & K Sugiyama (eds), Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13133 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 471-479, 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Virtual, Online, 12/1/21. https://doi.org/10.1007/978-3-030-91669-5_36

A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning. / Arumugam, Snehameena; Peng, Likai; Na, Jin Cheon et al.
Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings. ed. / Hao-Ren Ke; Chei Sian Lee; Kazunari Sugiyama. Springer Science and Business Media Deutschland GmbH, 2021. p. 471-479 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13133 LNCS).

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

TY - GEN

T1 - A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning

AU - Arumugam, Snehameena

AU - Peng, Likai

AU - Na, Jin Cheon

AU - Lin, Guangze

AU - Ganesh, Roopika

AU - Li, Xiaoyin

AU - Chen, Qing

AU - Ho, Shirley S.

AU - Cambria, Erik

PY - 2021

Y1 - 2021

N2 - Nuclear energy is one of controversial topics that affects people’s lives, and it is important for policy makers to analyze what people feel towards the subject. But manual analysis of related user-generated contents on social media platforms is a daunting task, and automatic data analysis and visualization come to help. So, in this research, we firstly developed a model for classifying the emotion of nuclear energy related tweets and another model for the aspect-based sentiment analysis of nuclear energy tweets using the BERT (Bidirectional Encoder Representations from Transformers). After that, we developed a prototype system for visualization of the analyzed results stored in the database. The user interface dashboards of the system allow users to monitor emotion and sentiment trends towards nuclear energy by analyzing recent nuclear energy tweets crawled weekly.

AB - Nuclear energy is one of controversial topics that affects people’s lives, and it is important for policy makers to analyze what people feel towards the subject. But manual analysis of related user-generated contents on social media platforms is a daunting task, and automatic data analysis and visualization come to help. So, in this research, we firstly developed a model for classifying the emotion of nuclear energy related tweets and another model for the aspect-based sentiment analysis of nuclear energy tweets using the BERT (Bidirectional Encoder Representations from Transformers). After that, we developed a prototype system for visualization of the analyzed results stored in the database. The user interface dashboards of the system allow users to monitor emotion and sentiment trends towards nuclear energy by analyzing recent nuclear energy tweets crawled weekly.

KW - Aspect based sentiment classification

KW - BERT

KW - Deep learning

KW - Emotion analysis

KW - Nuclear energy

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DO - 10.1007/978-3-030-91669-5_36

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AN - SCOPUS:85121909790

SN - 9783030916688

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

SP - 471

EP - 479

BT - Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings

A2 - Ke, Hao-Ren

A2 - Lee, Chei Sian

A2 - Sugiyama, Kazunari

PB - Springer Science and Business Media Deutschland GmbH

T2 - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021

Y2 - 1 December 2021 through 3 December 2021

ER -

Arumugam S, Peng L, Na JC, Lin G, Ganesh R, Li X et al. A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning. In Ke HR, Lee CS, Sugiyama K, editors, Towards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 471-479. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-91669-5_36

A Prototype System for Monitoring Emotion and Sentiment Trends Towards Nuclear Energy on Twitter Using Deep Learning

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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