Trend Topic Analysis using Latent Dirichlet Allocation (LDA) (Study Case: Denpasar People’s Complaints Online Website)
DOI:
https://doi.org/10.26555/jiteki.v5i1.13088Keywords:
Trend Topic, Topic Models, LDA, Gibbs sampling, DenpasarAbstract
According to the publication of the Central Bureau of Statistics 2017, the population of Denpasar people has increased to 914,300 people. The Increasing number of the population raises various problems that must be faced by the Denpasar’s Government. The variety of problems is in line with the increase in complaints data posted through Denpasar people’s complaints online website, which made it difficult to know the main topics of the problems. The purpose of this research is to find the main topics of complaints Denpasar residents quickly and efficiently. The method used to achieve the objective of the research is Latent Dirichlet Allocation topic models with Gibbs sampling parameter estimation. The number of topics obtained through the highest log-likelihood value -42,528.84, the value is in the number of topics 19. The trending topic was based on the highest topic probability, topic 4, with a topic probability value 0.055. Based on these results, the trend of a topic is on topic 4 which can be interpreted that many residents of Denpasar complained about damaged roads and requested to fix the roads.References
D. M. Blei and J. D. Lafferty, “Topic Models,†Text Min. Classif. Clust. Appl., 2009 available at: Google Scholar.
J. Tang, Z. Meng, X. L. Nguyen, Q. Mel, and M. Zhang, “Understanding the limiting factors of topic modeling via posterior contraction analysis,†in 31st International Conference on Machine Learning, ICML 2014, 2014 available at: Google Scholar.
J. Jayadharshini, R. Sivapriya, and S. Abirami, “Trend square: An Android Application for Extracting Twitter Trends Based on Location,†in Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018, 2018, doi: 10.1109/ICCTCT.2018.8551056
C. Zou, “Analyzing research trends on drug safety using topic modeling,†Expert Opin. Drug Saf., pp. 629-636 2018, doi: 10.1080/14740338.2018.1458838
M. C. Yang and H. C. Rim, “Identifying interesting Twitter contents using topical analysis,†Expert Syst. Appl., vol. 41, no. 19, pp. 4330-4336, 2014, doi: 10.1016/j.eswa.2013.12.051.
N. Dan N, R. Bellio , and T. Reutterer, “A note on latent rating regression for aspect analysis of user-generated contentâ€, In Proceedings of teh 33rd International Workshop on Statistical Modeling, vol. 1, Hrsg. Statistical Modelling Society , pp. 63-68, 2018 available at: Google
N. Schröder, “Using multidimensional item response theory models to explain multi-category purchasesâ€. Market ZFP, vol. 39, no. 2, pp.27–37, 2017 available at: Google Scholar
R. Zhang, Z. Cheng, J. Guan, and S. Zhou, “Exploiting topic modeling to boost metagenomic reads binning,†BMC Bioinformatics, 2015 available at: Google Scholar
Z. Wang, L. Li, C. Zhang, and Q. Huang, “Image-regulated graph topic model for cross-media topic detection,†in ACM International Conference Proceeding Series, 2015, doi: 10.1145/2808492.2808569.
D. Cao, R. Ji, D. Lin, and S. Li, “Visual sentiment topic model based microblog image sentiment analysis,†Multimed. Tools Appl., vol. 75, no. 15, pp.8955-8968, 2016, available at: Google Scholar.
J. Büschken, and G.M. Allenby, “Improving text analaysis using sentence conjunctions and punctuation,†SSRN, 2017 available at: Google Scholar
K.B. Park, and S.H. Ha., Mining user-generated contents to detect service failures with topic model,†Int J Comput Electr Autom Control Inform Eng, vol. 10, no. 8, pp. 1491–1496, 2016, doi: 10.5281/zenodo.1126073
Z. Yang, A. Kotov, A. Mohan, and S. Lu, “Parametric and non-parametric user-aware sentiment topic models,†in SIGIR 2015 - Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 413-422, 2015, doi: 10.1145/2766462.2767758.
H. R. Iqbal, M. A. Ashraf, and R. M. A. Nawab, “Predicting an author’s demographics from text using Topic Modeling approach,†in CEUR Workshop Proceedings, 2015, available at: Google Scholar.
M. E. Roberts, B. M. Stewart, and D. Tingley, “Navigating the local modes of big data: The case of topic models,†in Computational Social Science: Discovery and Prediction, 2016 avaliable at: Google.
E. M. Airoldi, D. M. Blei, E. A. Erosheva, and S. E. Fienberg, “Introduction to mixed membership models and methods,†in Handbook of Mixed Membership Models and Their Applications, 2014, available at: Google Scholar.
A. Galyardt, “Interpreting mixed membership models: Implications of erosheva’s representation theorem,†in Handbook of Mixed Membership Models and Their Applications, 2014 available at: Google Scholar.
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