Telkom University News Topic Modeling Using Latent Semantic Analysis (LSA) Method on Online News Portal
- Ihsan Ahsanu Amala Telkom University, Bandung, Indonesia
-
Donni Richasdy
*
Telkom University, Bandung,
Indonesia
- Mahendra Dwifebri Purbolaksono Telkom University, Bandung, Indonesia (*) Corresponding Author
Abstract
In this day and age, the development of online news portals regarding news is quite easy to access, online news portals are information that explains an event that has occurred or is happening with electronic media intermediaries, as well as news about Telkom University which is quite easily accessible through online news portals. A system has been designed that is capable of modeling Telkom University news topics. Modeling news topics is very interesting to be used as research material because the process of understanding each individual on the topics contained in the news is different, therefore topic modeling is needed to find out what topics are news about Telkom University. In this study, a Latent Semantic Analysis (LSA) model has been designed to carry out a topic modeling process that aims to make it easier for readers to understand news topics related to Telkom University, Latent Semantic Analysis (LSA) is a mathematical method in finding hidden topics by analyzing the structure semantics of the text. After doing several research scenarios, the best coherence score was 0.524 with a total of six topics.
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References
M. Tanikawa, “What Is News? What Is the Newspaper? The Physical, Functional, and Stylistic Transformation of Print Newspapers, 1988-2013 MIKI TANIKAWA,” 2017. [Online]. Available: http://ijoc.org.
G. Costa and R. Ortale, “Document clustering and topic modeling: A unified bayesian probabilistic perspective,” in Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, Nov. 2019, vol. 2019-November, pp. 278–285. doi: 10.1109/ICTAI.2019.00047.
T. Iwata, T. Hirao, and N. Ueda, “Topic Models for Unsupervised Cluster Matching,” IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 4, pp. 786–795, Apr. 2018, doi: 10.1109/TKDE.2017.2778720.
A. Moodley and V. Marivate, “Topic modelling of news articles for two consecutive elections in South Africa,” in 2019 6th International Conference on Soft Computing and Machine Intelligence, ISCMI 2019, Nov. 2019, pp. 131–136. doi: 10.1109/ISCMI47871.2019.9004342.
Y. Kalepalli, T. Shaik, D. Pasupuleti, and S. Manne, “Effective Comparison of LDA with for Topic Modelling,” International Confrence on Intelligent Computing Control System (ICICCS)), pp. 1245–1250, 2020.
K. Rajendra Prasad, M. Mohammed, and R. M. Noorullah, “Visual topic models for healthcare data clustering,” Evolutionary Intelligence, vol. 14, no. 2, pp. 545–562, Jun. 2021, doi: 10.1007/s12065-019-00300-y.
D. Sarkar, Text Analytics with Python. Apress, 2016. doi: 10.1007/978-1-4842-2388-8.
P. Kherwa and P. Bansal, “Latent Semantic Analysis: An Approach to Undestand Semantic of Text,” International Conference on Current Trends in Computer, Electrical, Electronics and Communication, pp. 870–874, 2017.
P. P. G. Neogi, A. K. Das, S. Goswami, and J. Mustafi, “Topic Modeling for Text Classification,” in Advances in Intelligent Systems and Computing, 2020, vol. 937, pp. 395–407. doi: 10.1007/978-981-13-7403-6_36.
H. A. Fathan, P. E. Cergas, W. Kurniawan, G. Akbar, and P. Ridwan, “Twitter Topic Modeling on Football News,” International Conference on Computer and Communication Systems, pp. 467–471, 2018.
S. Syed and M. Spruit, “Full-Text or abstract? Examining topic coherence scores using latent dirichlet allocation,” in Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017, Jul. 2017, vol. 2018-January, pp. 165–174. doi: 10.1109/DSAA.2017.61.
Shelly Maysar, “13 Portal Berita Online Terbaik di Indonesia,” Akudigital.com, Dec. 04, 2021.
S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,” International Journal of Computer Applications, vol. 181, no. 1, pp. 25–29, Jul. 2018, doi: 10.5120/ijca2018917395.
K. Al-Sabahi CVTE, K. Al-Sabahi, Z. Zuping, and Y. Kang, “Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings,” 2018. [Online]. Available: https://www.researchgate.net/publication/326290389
F. Yi, B. Jiang, and J. Wu, “Topic Modeling for Short Texts via Word Embedding and Document Correlation,” IEEE Access, vol. 8, pp. 30692–30705, 2020, doi: 10.1109/ACCESS.2020.2973207.
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