skip to main content
10.1145/3512576.3512608acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicitConference Proceedingsconference-collections
research-article

An analysis of Disaster Risk Suggestions using Latent Dirichlet Allocation and Hierarchical Dirichlet Process (Nonparametric LDA)

Authors Info & Claims
Published:11 April 2022Publication History

ABSTRACT

Qualitative data is part of the things that most social scientists would deal with. In this study, qualitative Disaster Risk Reduction suggestions were analyzed using topic modeling techniques. Latent Dirichlet allocation is one of the topic modeling that was utilized in this study. The ideal number of topic models being generated for LDA is 10 with a score of 530.1495. Hierarchical Dirichlet Process model was also used to get the topic models from the corpus. The HDP model generated 11 topic models with a log-likelihood score of -4.08997. The topic models being generated by the parametric LDA and non-parametric LDA are almost similar. To analyze the result of the topic models, open coding technique was utilized. The following narratives were the focus of the DRR responses: Solid waste management and improve drainage system, Relief and Emergency Plan and Early warning system and Disaster Preparedness.

References

  1. [UNISDR. (2011). National Disaster Risk Reduction and Management Plan (NDRRMP), 70. Retrieved from http://www.ndrrmc.gov.ph/attachments/article/567/Signed_NDRRMP.pdfGoogle ScholarGoogle Scholar
  2. Philippines, Department of Education (2008). Disaster Risk Reduction Resource Manual.Google ScholarGoogle Scholar
  3. Anselm L Strauss. 1987. Qualitative analysis for social scientists. Cambridge University PressGoogle ScholarGoogle Scholar
  4. Jasy Liew Suet Yan, Nancy McCracken, Shichun Zhou and Kevin Crowston. 2014. Optimizing Features in Active Machine Learning for Complex Qualitative Content Analysis. ACL 2014: 44.Google ScholarGoogle Scholar
  5. McCallum, A. K. (2002). MALLET: A Machine Learning for Language Toolkit. Retrieved from http://mallet.cs.umass.eduGoogle ScholarGoogle Scholar
  6. Latent Dirichlet Allocation for Beginners: A high level intuition. Retrieved from https://medium.com/@pratikbarhate/latent-diric hletallocation-for-beginners-a-high-level-intuitio n-23f8a5cbad71Google ScholarGoogle Scholar
  7. Wang, C., Paisley, J., & Blei, D. (2011, June). Online variational inference for the hierarchical Dirichlet process. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (pp. 752-760). JMLR Workshop and Conference Proceedings.Google ScholarGoogle Scholar
  8. Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic variational inference. Journal of Machine Learning Research, 14(5).Google ScholarGoogle Scholar
  9. Malasakit. https://opinion.berkeley.edu/pcari/en/landing/Google ScholarGoogle Scholar
  10. Nonnecke, B., Mohanty, S., Lee, A., Lee, J., Beckman, S., Mi, J., ... & Goldberg, K. (2018, October). Malasakit 2.0: A Participatory Online Platform with Feature Phone Integration and Voice Recognition for Crowdsourcing Disaster Risk Reduction Strategies in the Philippines. In 2018 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 1-6). IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  11. Gorro, K., Ancheta, J. R., Capao, K., Oco, N., Roxas, R. E., Sabellano, M. J., ... & Goldberg, K. (2017, December). Qualitative data analysis of disaster risk reduction suggestions assisted by topic modeling and word2vec. In 2017 International Conference on Asian Language Processing (IALP) (pp. 293-297). IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  12. Bui, S. M. G., Gorro, K., Aquino, G. A., & Sabellano, M. J. (2017, December). An analysis of DRR suggestions using K-means clustering. In Proceedings of the 2017 International Conference on Information Technology (pp. 76-80).Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Bakharia, A., Bruza, P., Watters, J., Narayan, B., and Sitbon, L. 2016. Interactive Topic Modeling for aiding Qualitative Content Analysis. In Proceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval (CHIIR '16). ACM, New York, NY, USA, 213-222. DOI: https://doi.org/10.1145/2854946.2854960Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Chen, N. C., Kocielnik, R., and Drouhard, M. 2016. Challenges of Applying Machine Learning to Qualitative Coding. Retrieved from https://faculty.washington.edu/aragon/pubs/textv isdrg_hcml2016.pdfTierney Patrick. 2012. A Qualitative Analysis Framework Using Natural Language Processing and Graph Theory. Retrieved from http://www.irrodl.org/index.php/irrodl/article/vie w/1240/2363Google ScholarGoogle Scholar
  15. Ancheta, J. R., Gorro, K. D., & Uy, M. A.D. (2020). # Walangpasok on Twitter: Natural language processing as a method for analyzing tweets on class suspensions in the Philippines. In 2020 12th International Conference on Knowledge and Smart Technology (KST) (pp. 103-108). IEEE.Google ScholarGoogle ScholarCross RefCross Ref
  16. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781Google ScholarGoogle Scholar
  17. Bhatia, S., Lau, J. H., & Baldwin, T. (2017). An Automatic Approach for Document-level Topic Model Evaluation. arXiv preprint arXiv:1706.05140Google ScholarGoogle Scholar
  18. Schnabel, T., Labutov, I., Mimno, D. M., & Joachims, T. (2015, September). Evaluation methods for unsupervised word embeddings. In EMNLP (pp. 298 - 307)Google ScholarGoogle Scholar
  19. Gorro, K. D., Ali, M., Gorro, K. D., Ancheta, J. R., (2020, December) The 8th International Conference on Information Technology: IoT and Smart City, pp 69-73• https://doi.org/10.1145/3446999.3447012Google ScholarGoogle ScholarDigital LibraryDigital Library

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in
  • Published in

    cover image ACM Other conferences
    ICIT '21: Proceedings of the 2021 9th International Conference on Information Technology: IoT and Smart City
    December 2021
    584 pages
    ISBN:9781450384971
    DOI:10.1145/3512576

    Copyright © 2021 ACM

    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 11 April 2022

    Permissions

    Request permissions about this article.

    Request Permissions

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited
  • Article Metrics

    • Downloads (Last 12 months)20
    • Downloads (Last 6 weeks)0

    Other Metrics

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format