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.
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References
Xu, C., Xu, Y., Wang, S., Liu, Y., Zhu, C., McAuley, J.: Small models are valuable plugins for large language models. arXiv preprint arXiv:2305.08848 (2023)
Zhang, C., Li, Q., Song, D.: Aspect-based sentiment classification with aspect-specific graph convolutional networks. arXiv preprint arXiv:1909.03477 (2019)
Do, H.H., Prasad, P.W.C., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst. Appl. 118, 272–299 (2019)
Schouten, K., Frasincar, F.: Survey on aspect-level sentiment analysis. IEEE Trans. Knowl. Data Eng. 28(3), 813–830 (2015)
Tan, X., Cai, Y., Xu, J., Leung, H.-F., Chen, W., Li, Q.: Improving aspect-based sentiment analysis via aligning aspect embedding. Neurocomputing 383, 336–347 (2020)
Fei, H., Chua, T.-S., Li, C., Ji, D., Zhang, M., Ren, Y.: On the robustness of aspect-based sentiment analysis: rethinking model, data, and training. ACM Trans. Inf. Syst. 41(2), 1–32 (2022)
Yang, J., Yang, J.: Aspect based sentiment analysis with self-attention and gated convolutional networks. In: 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS), pp. 146–149. IEEE (2020)
Lim, S., Yang, W.W., Leong, C.-H., Hong, J.: Reconfiguring the Singapore identity space: beyond racial harmony and survivalism. Int. J. Intercult. Relat. 43, 13–21 (2014)
Huat, C.B.: Multiculturalism in Singapore: an instrument of social control. Race & Class 44(3), 58–77 (2003)
Velayutham, S.: Races without racism?: Everyday race relations in Singapore. Identities 24(4), 455–473 (2017)
Chew, P.K.H.: Racism in Singapore: a review and recommendations for future research. J. Pac. Rim Psychol. 12, e5 (2018)
Ling Ooi, G.: The role of the developmental state and interethnic relations in Singapore. Asian Ethnicity 6(2), 109–120 (2005)
Chew, P.K.H., Young, J.L., Tan, G.P.K.: Racism and the Pinkerton syndrome in Singapore: effects of race on hiring decisions. J. Pac. Rim Psychol. 13, e16 (2019)
Brownlee, J.: Imbalanced classification with Python: better metrics, balance skewed classes, cost-sensitive learning. Mach. Learn. Mastery (2020)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Philip Kegelmeyer, W.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Ubani, S., Polat, S.O., Nielsen, R.: ZeroShotDataAug: generating and augmenting training data with ChatGPT. arXiv preprint arXiv:2304.14334 (2023)
Li, X., Qiu, X.: Finding supporting examples for in-context learning. arXiv preprint arXiv:2302.13539 (2023)
Dong, Q., et al.: A survey for in-context learning. arXiv preprint arXiv:2301.00234 (2022)
Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)
Min, S., et al.: Rethinking the role of demonstrations: what makes in-context learning work? arXiv preprint arXiv:2202.12837 (2022)
SeatGeek, FuzzyWuzzy, GitHub Repository (2020). https://github.com/seatgeek/fuzzywuzzy
Zeng, B., Yang, H., Xu, R., Zhou, W., Han, X.: LCF: a local context focus mechanism for aspect-based sentiment classification. Appl. Sci. 9(16), 3389 (2019)
Heng, Y.: PyABSA: A Modularized Framework for Reproducible Aspect-Based Sentiment Analysis [Computer software] (2023)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Kim, D.S., Kim, J.W.: Public opinion sensing and trend analysis on social media: a study on nuclear power on Twitter. Int. J. Multim. Ubiquit. Eng. 9(11), 373–384 (2014)
Acknowledgment
This work is supported by DSO National Laboratories in Singapore (No. DSOCL21092). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the DSO National Laboratories.
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Tudi, M.R., 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: Goh, D.H., Chen, SJ., Tuarob, S. (eds) Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration. ICADL 2023. Lecture Notes in Computer Science, vol 14457. Springer, Singapore. https://doi.org/10.1007/978-981-99-8085-7_5
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DOI: https://doi.org/10.1007/978-981-99-8085-7_5
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