Evaluation of Creative Economy and Tourism Industry Trends based on LDA Analysis with BERTopic
DOI:
https://doi.org/10.31849/digitalzone.v15i2.23796Keywords:
Trends, Creative economy tourism, Topic model, LDA, BERTopicAbstract
Creative economy and tourism industry have a role in contributing country's foreign exchange. Efforts continue to be improved by utilizing social media. Latent Dirichlet allocation (LDA) and BERTopic topic model are used as topic models for creative economy and tourism trend analysis. The evaluation was carried out using a coherence matrix, topic distribution, similarity, and topic identification over the last five-years period. BERTopic has a higher coherence value of 0.53 compared to LDA 0.30 although the number of outlier topics dominates. The identification of the most relevant main topic trends is finance, travel, beaches and investment. These themes are interrelated in driving the growth of the creative economy and tourism, which increases local income and innovation in related sectors. BERTopic identifies hidden topics such as bitcoin cryptocurrency. In contrast, LDA provides a more even distribution of topics, revealing traditional trends such as beach tourism and travel. The evaluation offers key recommendations on creative economy and tourism policies to innovations about investment.
References
M. Ashoer et al., Ekonomi Pariwisata, vol. 5, no. 3. 2021.
A. S. E. S. Sri Hardianti Sartikan, Muhammad hasan, Ekonomi Kreatif Agus Syam, no. July. 2022.
KEMENPAREKRAF, “Outlook Pariwisata dan Ekonomi Kreatif,” Deputi Bid. Kebijak. Strateg. Kementeri. Pariwisata dan Ekon. Kreat. Badan Pariwisata dan Ekon. Kreat. Republik Indones. Jakarta – Indones., pp. 1–68, 2020, [Online]. Available: https://bankdata.kemenparekraf.go.id/upload/document_satker/a6d2d69c8056a29657be2b5ac3107797.pdf.
Kementrian Pariwisata dan Ekonomi Kreatif, “Wisatawan Mancanegara Perkembangan Januari 2024,” pp. 4–4, 2024.
We Are Social Meltwater, “Digital 2023 Report,” Meltwater, p. 213, 2023.
A. Yunita, S. F. Telaumbanua, and A. Irawan, “The Tweetology of New and Renewable Energy in Indonesia,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 17, no. 2, p. 127, 2023, https://doi.org/10.22146/ijccs.81397.
F. Gurcan, A. Ayaz, G. G. Menekse Dalveren, and M. Derawi, “Business Intelligence Strategies, Best Practices, and Latest Trends: Analysis of Scientometric Data from 2003 to 2023 Using Machine Learning,” Sustain., vol. 15, no. 13, 2023, https://doi.org/10.3390/su15139854.
W. Kang, Y. Kim, H. Kim, and J. Lee, “An Analysis of Research Trends on Language Model Using BERTopic,” Proc. - 2023 Congr. Comput. Sci. Comput. Eng. Appl. Comput. CSCE 2023, no. 2022, pp. 168–172, 2023, https://doi.org/10.1109/CSCE60160.2023.00032.
L. Yijia, “Comparison of LDA and BERTopic in News Topic Modeling : A Case Study of The New York Times ’ Reports on China,” vol. 7, no. June, pp. 47–51, 2024, https://doi.org/10.55014/pij.v7i3.616.
F. Bianchi, S. Terragni, and D. Hovy, “Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence,” ACL-IJCNLP 2021 - 59th Annu. Meet. Assoc. Comput. Linguist. 11th Int. Jt. Conf. Nat. Lang. Process. Proc. Conf., vol. 2, pp. 759–766, 2021, https://doi.org/10.18653/v1/2021.acl-short.96.
A. E. Ezugwu et al., “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects,” Eng. Appl. Artif. Intell., vol. 110, no. February, p. 104743, 2022, https://doi.org/10.1016/j.engappai.2022.104743.
J. Devlin, M.-W. Chang, K. Lee, K. T. Google, and A. I. Language, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Naacl-Hlt 2019, no. Mlm, pp. 4171–4186, 2018, [Online]. Available: https://aclanthology.org/N19-1423.pdf.
C. D. P. Laureate, W. Buntine, and H. Linger, A systematic review of the use of topic models for short text social media analysis, vol. 56, no. 12. Springer Netherlands, 2023.
Y. Sahria and D. Hatta Fudholi, “Analisis Topik Penelitian Kesehatan di Indonesia Menggunakan Metode Topic Modeling LDA (Latent Dirichlet Allocation),” Masa Berlaku Mulai, vol. 1, no. 3, pp. 336–344, 2017.
A. Panichella, “A Systematic Comparison of search-Based approaches for LDA hyperparameter tuning,” Inf. Softw. Technol., vol. 130, p. 106411, 2021, https://doi.org/10.1016/j.infsof.2020.106411.
S. J. Blair, Y. Bi, and M. D. Mulvenna, “Aggregated topic models for increasing social media topic coherence,” Appl. Intell., vol. 50, no. 1, pp. 138–156, 2020, htpps://doi.org/10.1007/s10489-019-01438-z.
M. M. Mostafa, A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research, vol. 57, no. 4. Springer Netherlands, 2023.
R. Panchendrarajan and A. Saxena, “Topic-based influential user detection: a survey,” Appl. Intell., vol. 53, no. 5, pp. 5998–6024, 2023, doi: 10.1007/s10489-022-03831-7.
S. Hwang, E. Flavin, and J. E. Lee, “Exploring research trends of technology use in mathematics education: A scoping review using topic modeling,” Educ. Inf. Technol., vol. 28, no. 8, pp. 10753–10780, 2023, doi: 10.1007/s10639-023-11603-0.
Z. A. Hasibuan, “Towards using universal big data in artificial intelligence research and development to gain meaningful insights and automation systems,” 2020 Int. Work. Big Data Inf. Secur. IWBIS 2020, pp. 9–15, 2020, https://doi.org/10.1109/IWBIS50925.2020.9255497.
O. Ozyurt, H. Özköse, and A. Ayaz, “Evaluating the latest trends of Industry 4.0 based on LDA topic model,” J. Supercomput., no. 0123456789, 2024, https://doi.org/10.1007/s11227-024-06247-x.
M. Mishra, S. K. Vishwakarma, L. Malviya, and S. Anjana, “Temporal analysis of computational economics: a topic modeling approach,” Int. J. Data Sci. Anal., 2024, https://doi.org/10.1007/s41060-024-00596-9.
W. Chen, F. Rabhi, W. Liao, and I. Al-Qudah, “Leveraging State-of-the-Art Topic Modeling for News Impact Analysis on Financial Markets: A Comparative Study,” Electron., vol. 12, no. 12, 2023, https://doi.org/10.3390/electronics12122605.
C. Lalk et al., “Measuring Alliance and Symptom Severity in Psychotherapy Transcripts Using Bert Topic Modeling,” Adm. Policy Ment. Heal. Ment. Heal. Serv. Res., vol. 51, no. 4, pp. 509–524, 2024, doi: 10.1007/s10488-024-01356-4.
I. Lauriola, A. Lavelli, and F. Aiolli, “An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools,” Neurocomputing, vol. 470, no. xxxx, pp. 443–456, 2022, doi: 10.1016/j.neucom.2021.05.103.
B. Yin and C. H. Yuan, “Detecting latent topics and trends in blended learning using LDA topic modeling,” Educ. Inf. Technol., vol. 27, no. 9, pp. 12689–12712, 2022, htpps://doi.org/10.1007/s10639-022-11118-0.
Y. Chen, Z. Xie, and D. K. W. Chiu, “Analytics of motivational factors of educational video games: LDA topic modeling and the 6 C’s learning motivation model,” Educ. Inf. Technol., 2024, https://doi.org/10.1007/s10639-024-12726-8.
D. M. Blei, A. Y. Ng, and M. T. Jordan, “Latent dirichlet allocation,” Adv. Neural Inf. Process. Syst., no. January 2001, 2002.
M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” 2020.
A. Farea, S. Tripathi, G. Glazko, and F. Emmert-Streib, “Investigating the optimal number of topics by advanced text-mining techniques: Sustainable energy research,” Eng. Appl. Artif. Intell., vol. 136, no. PA, p. 108877, 2024, https://doi.org/10.1016/j.engappai.2024.108877.
H. Zankadi, A. Idrissi, N. Daoudi, and I. Hilal, “Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques,” Educ. Inf. Technol., vol. 28, no. 5, pp. 5567–5584, 2023, https://doi.org/10.1007/s10639-022-11373-1.
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