Penerapan BERTopic dan Analisis Sentimen Leksikal Pada Ulasan Relevan di Google Maps Mengenai Universitas Pamulang
- Nilovar Asyiah Universitas Pamulang, Tangerang Selatan, Indonesia
-
Widodo Aktavia
*
Universitas Pamulang, Tangerang Selatan,
Indonesia
(*) Corresponding Author
Abstract
The rapid advancement of information technology has encouraged the public to actively share reviews through digital platforms such as Google Maps. These reviews are not only informative but also reflect real user opinions and experiences regarding places or institutions, including higher education institutions. This study aims to analyze the main topics and sentiment classification contained in Google Maps reviews related to Universitas Pamulang. The approach used in this research combines two main methods. First, topic modeling is conducted using BERTopic, a modern technique based on transformer embeddings and HDBSCAN clustering algorithms, which can capture the semantic context of text more deeply. Second, sentiment analysis is performed using a lexicon-based approach, applying an Indonesian sentiment lexicon to efficiently identify the polarity of opinions without requiring model training.The data analyzed were collected through web scraping of relevant public reviews on Google Maps across four Universitas Pamulang locations: Central Campus, Viktor Campus, Witanaharja Campus, and Unpam Serang. The analysis revealed several dominant topics such as academic services, campus facilities, and bureaucracy. The majority of sentiments identified were neutral to positive, although negative opinions were also found in certain aspects. These findings are expected to serve as strategic input for the university to enhance service quality and strengthen its institutional image in the digital landscape.
Downloads
References
F. Akbar, Hadiyanto, and C. E. Widodo, “Sentiment analysis of data on Google Maps reviews regarding tourism on Keraton Kasepuhan Cirebon using the lexicon based method,” Proc. 3rd Int. Conf. Adv. Inf. Sci. Dev., pp. 19–24, 2023, doi: 10.5220/0012440100003848.
C.-C. Chen and C.-C. Chang, “Evaluating public library services through user-generated content: Social network analysis of Google Maps reviews,” Preprints, May 2024, doi: 10.20944/preprints202405.0619.v1.
F. Kitsios, E. Mitsopoulou, E. Moustaka, and M. Kamariotou, “User-generated content in social media: A twenty-year bibliometric analysis in hospitality,” Information, vol. 13, no. 12, p. 574, Dec. 2022, doi: 10.3390/info13120574.
A. Göçen, M. M. Ibrahim, and A. U. I. Khan, “Public attitudes toward higher education using sentiment analysis and topic modeling,” Discover Artificial Intelligence, vol. 4, no. 1, p. 83, Nov. 2024, doi: 10.1007/s44163-024-00195-4.
F. Yahya, D. E. Ratnawati, and B. Rahayudi, “Analisis sentimen ulasan pengguna dari Google Maps menggunakan metode Long Short-Term Memory (studi kasus: Rumah Sakit Gatoel),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 9, no. 4, pp. 1–11, 2025. [Online]. Available: http://j-ptiik.ub.ac.id
J. Wu, P. Qu, B. Zhang, and Z. Zhou, “Sentiment analysis in social media: Leveraging BERT for enhanced accuracy,” Journal of Industrial Engineering and Applied Sciences, vol. 2, no. 4, 2024. [Online]. Available: https://doi.org/10.5281/zenodo.13144389
M. Grootendorst, “BERTopic: Neural topic modeling with a class-based TF-IDF procedure,” arXiv preprint, arXiv:2203.05794, Mar. 2022. [Online]. Available: http://arxiv.org/abs/2203.05794
N. Khodeir and F. Elghannam, “Efficient topic identification for urgent MOOC forum posts using BERTopic and traditional topic modeling techniques,” Education and Information Technologies, vol. 30, no. 5, pp. 5501–5527, Apr. 2025, doi: 10.1007/s10639-024-13003-4.
D. Kumar and M. Weissenberger-Eibl, “Artificial intelligence driven trend forecasting: Integrating BERT topic modelling and generative artificial intelligence for semantic insights,” in R&D Management Conference 2024, 2024, doi: 10.24406/publica-3456.
Y. Jin, Using Text Mining and BERTopic, Master’s thesis, University of California, 2022. [Online]. Available: https://escholarship.org/uc/item/69m2f87d
F. T. Saputra, S. H. Wijaya, Y. Nurhadryani, and Defina, “Lexicon addition effect on lexicon-based of Indonesian sentiment analysis on Twitter,” in Proc. 2020 Int. Conf. Informatics, Multimedia, Cyber and Inf. Syst. (ICIMCIS), Nov. 2020, pp. 136–141, doi: 10.1109/ICIMCIS51567.2020.9354269.
A. Rufaida, A. Permanasari, and N. Setiawan, “Lexicon-based sentiment analysis using Inset dictionary: A systematic literature review,” in Proc. 7th EAI Int. Conf. Big Data Innov. Smart Cities (EAI BDISC), Oct. 2022, doi: 10.4108/eai.5-10-2022.2327474..
Y. Fauziah, B. Yuwono, and A. S. Aribowo, “Lexicon-based sentiment analysis in Indonesia languages: A systematic literature review,” in RSF Conf. Ser. Eng. Technol., vol. 1, no. 1, Dec. 2021, pp. 363–367, doi: 10.31098/cset.v1i1.397.
I. Fatan and H. Irawan, “Twitter, Instagram, YouTube speak: Understanding sentiments on LRT Jabodebek services via Inset lexicon, IndoBERT and BERTopic approaches,” J. Electr. Syst., vol. 20, no. 4s, pp. 1028–1035, Apr. 2024, doi: 10.52783/jes.2147..
S. Samsir, R. S. Saragih, S. Subagio, R. Aditiya, and R. Watrianthos, “BERTopic modeling of natural language processing abstracts: Thematic structure and trajectory,” J. Media Inform. Budidarma, vol. 7, no. 3, p. 1514, Jul. 2023, doi: 10.30865/mib.v7i3.6426.
S. Ramadhan, T. Siswanto, and S. Sari, “Sentiment analysis and topic modelling of candidate news in the 2024 general election on Twitter social media using latent Dirichlet allocation (LDA) method,” Intelmatics, vol. 5, no. 1, pp. 33–41, Feb. 2025, doi: 10.25105/v5i1.21058.
H. Lee, S. H. Lee, K. R. Lee, and J. H. Kim, “ESG discourse analysis through BERTopic: Comparing news articles and academic papers,” Comput. Mater. Contin., vol. 75, no. 3, pp. 6023–6037, 2023, doi: 10.32604/cmc.2023.039104..
L. McInnes, J. Healy, and J. Melville, “UMAP: Uniform manifold approximation and projection for dimension reduction,” arXiv preprint arXiv:1802.03426, Sep. 2020, [Online]. Available: http://arxiv.org/abs/1802.03426
O. Babalola, B. Ojokoh, and O. Boyinbode, “Comprehensive evaluation of LDA, NMF, and BERTopic’s performance on news headline topic modeling,” J. Comput. Theor. Appl., vol. 2, no. 2, pp. 268–289, Nov. 2024, doi: 10.62411/jcta.11635.
V. Sharifian-Attar, S. De, S. Jabbari, J. Li, H. Moss, and J. Johnson, “Analysing longitudinal social science questionnaires: Topic modelling with BERT-based embeddings,” in Proc. 2022 IEEE Int. Conf. on Big Data (Big Data), Osaka, Japan, Dec. 2022, pp. 5558–5567, doi: 10.1109/BigData55660.2022.10020678.
W. Yang, X. Li, J. Zhang, K. Wang, and L. Chen, “A two-stage feature extraction approach for green energy consumers in retail electricity markets using clustering and TF–IDF algorithms,” Energy Eng., vol. 122, no. 5, pp. 1697–1713, 2025, doi: 10.32604/ee.2025.060571.
C. H. Hill, J. E. Fresneda, and M. Anandarajan, “The wisdom of the lexicon crowds: Leveraging on decades of lexicon-based sentiment analysis for improved results,” J. Big Data, vol. 12, no. 1, p. 129, May 2025, doi: 10.1186/s40537-025-01186-7.
N. H. Md Saad, A. S. Zainul Abidi, Z. Yaacod, M. M. Mohd Ali, and Z. Kun, “Twitter sentiment analysis on Meta: A lexicon-based analysis using RapidMiner,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 6, pp. 259–270, Jul. 2023, doi: 10.17762/ijritcc.v11i6.7561.
R. Catelli, S. Pelosi, C. Comito, C. Pizzuti, and M. Esposito, “Lexicon-based sentiment analysis to detect opinions and attitude towards COVID-19 vaccines on Twitter in Italy,” Comput. Biol. Med., vol. 158, p. 106876, May 2023, doi: 10.1016/j.compbiomed.2023.106876.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan BERTopic dan Analisis Sentimen Leksikal Pada Ulasan Relevan di Google Maps Mengenai Universitas Pamulang
Pages: 2129-2138
Copyright (c) 2025 Nilovar Asyiah, Widodo Aktavia
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
