Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT

Manoj Reddy Tudi; Jin Cheon Na; Meky Liu; Hongjin Chen; Yiqing Dai; Li Yang

doi:10.1007/978-981-99-8085-7_5

Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT

Manoj Reddy Tudi^*, Jin Cheon Na, Meky Liu, Hongjin Chen, Yiqing Dai, Li Yang

^*Corresponding author for this work

Nanyang Technological University

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

1 Citation (Scopus)

Abstract

This study employs Aspect-Based Sentiment Analysis (ABSA) and advanced AI methodologies to analyze public sentiment on racial issues in Singapore from 2018–2023. By utilizing synthetic data generation and In-Context Learning with ChatGPT API, we enhanced the performance of our ABSA model. Our findings highlight the utility of these methods in overcoming data imbalance and providing a comprehensive understanding of sentiment polarity associated with racially related aspect terms. Despite the higher cost of using sophisticated language models, the study underscores the potential of these techniques in offering nuanced insights into complex societal dynamics, illuminating a promising path for future research in sentiment analysis.

Original language	English
Title of host publication	Leveraging Generative Intelligence in Digital Libraries
Subtitle of host publication	Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings
Editors	Dion H. Goh, Shu-Jiun Chen, Suppawong Tuarob
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	41-55
Number of pages	15
ISBN (Print)	9789819980840
DOIs	https://doi.org/10.1007/978-981-99-8085-7_5
Publication status	Published - 2023
Externally published	Yes
Event	25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023 - Taipei, Taiwan, Province of China Duration: Dec 4 2023 → Dec 7 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14457 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023
Country/Territory	Taiwan, Province of China
City	Taipei
Period	12/4/23 → 12/7/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Aspect-Based Sentiment Analysis (ABSA)
ChatGPT API
In-Context Learning
Racial Issues
Social Media Analysis
Synthetic Data Generation

Access to Document

10.1007/978-981-99-8085-7_5

Cite this

Tudi, M. R., Na, J. C., Liu, M., Chen, H., Dai, Y., & Yang, L. (2023). Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT. In D. H. Goh, S.-J. Chen, & S. Tuarob (Eds.), Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings (pp. 41-55). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14457 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8085-7_5

Tudi, Manoj Reddy ; Na, Jin Cheon ; Liu, Meky et al. / Aspect-Based Sentiment Analysis of Racial Issues in Singapore : Enhancing Model Performance Using ChatGPT. Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. editor / Dion H. Goh ; Shu-Jiun Chen ; Suppawong Tuarob. Springer Science and Business Media Deutschland GmbH, 2023. pp. 41-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{89f15c174e4140708ce9417a092069de,

title = "Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT",

abstract = "This study employs Aspect-Based Sentiment Analysis (ABSA) and advanced AI methodologies to analyze public sentiment on racial issues in Singapore from 2018–2023. By utilizing synthetic data generation and In-Context Learning with ChatGPT API, we enhanced the performance of our ABSA model. Our findings highlight the utility of these methods in overcoming data imbalance and providing a comprehensive understanding of sentiment polarity associated with racially related aspect terms. Despite the higher cost of using sophisticated language models, the study underscores the potential of these techniques in offering nuanced insights into complex societal dynamics, illuminating a promising path for future research in sentiment analysis.",

keywords = "Aspect-Based Sentiment Analysis (ABSA), ChatGPT API, In-Context Learning, Racial Issues, Social Media Analysis, Synthetic Data Generation",

author = "Tudi, {Manoj Reddy} and Na, {Jin Cheon} and Meky Liu and Hongjin Chen and Yiqing Dai and Li Yang",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023 ; Conference date: 04-12-2023 Through 07-12-2023",

year = "2023",

doi = "10.1007/978-981-99-8085-7_5",

language = "English",

isbn = "9789819980840",

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",

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editor = "Goh, {Dion H.} and Shu-Jiun Chen and Suppawong Tuarob",

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Tudi, MR, Na, JC, Liu, M, Chen, H, Dai, Y & Yang, L 2023, Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT. in DH Goh, S-J Chen & S Tuarob (eds), Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14457 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 41-55, 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Taipei, Taiwan, Province of China, 12/4/23. https://doi.org/10.1007/978-981-99-8085-7_5

Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT. / Tudi, Manoj Reddy; Na, Jin Cheon; Liu, Meky et al.
Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. ed. / Dion H. Goh; Shu-Jiun Chen; Suppawong Tuarob. Springer Science and Business Media Deutschland GmbH, 2023. p. 41-55 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14457 LNCS).

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

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AU - Dai, Yiqing

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AB - This study employs Aspect-Based Sentiment Analysis (ABSA) and advanced AI methodologies to analyze public sentiment on racial issues in Singapore from 2018–2023. By utilizing synthetic data generation and In-Context Learning with ChatGPT API, we enhanced the performance of our ABSA model. Our findings highlight the utility of these methods in overcoming data imbalance and providing a comprehensive understanding of sentiment polarity associated with racially related aspect terms. Despite the higher cost of using sophisticated language models, the study underscores the potential of these techniques in offering nuanced insights into complex societal dynamics, illuminating a promising path for future research in sentiment analysis.

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ER -

Tudi MR, Na JC, Liu M, Chen H, Dai Y, Yang L. Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT. In Goh DH, Chen SJ, Tuarob S, editors, Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 41-55. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-8085-7_5

Aspect-Based Sentiment Analysis of Racial Issues in Singapore: Enhancing Model Performance Using ChatGPT

Abstract

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Bibliographical note

ASJC Scopus Subject Areas

Keywords

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