Open Access
Issue
E3S Web Conf.
Volume 317, 2021
The 6th International Conference on Energy, Environment, Epidemiology, and Information System (ICENIS 2021)
Article Number 05020
Number of page(s) 9
Section Information System Management and Environment
DOI https://doi.org/10.1051/e3sconf/202131705020
Published online 05 November 2021
  1. BNPB, “No Title,” DesInventar - Profile, 2020. https://dibi.bnpb.go.id/DesInventar/profilet%0Aab.jsp?countrycode=id&continue=y. [Google Scholar]
  2. P. Lei, G. Marfia, G. Pau, and R. Tse, “Can we monitor the natural environment analyzing online social network posts? A literature review,” Online Soc. Networks Media, vol. 5, pp. 51–60, 2018, doi: 10.1016/j.osnem.2017.12.001. [Google Scholar]
  3. K. Garimella and G. Tyson, “Whatsapp, doc? A first look at Whatsapp public group data,” 12th Int. AAAI Conf. Web Soc. Media, ICWSM 2018, no. Icwsm, pp. 511–517, 2018. [Google Scholar]
  4. K. Stock, “Mining location from social media: A systematic review,” Comput. Environ. Urban Syst., vol. 71, no. May, pp. 209–240, 2018, doi: 10.1016/j.compenvurbsys.2018.05.007. [Google Scholar]
  5. C. Fan, M. Esparza, J. Dargin, F. Wu, B. Oztekin, and A. Mostafavi, “Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters,” Comput. Environ. Urban Syst., vol. 83, no. May, p. 101514, 2020, doi: 10.1016/j.compenvurbsys.2020.101514. [Google Scholar]
  6. A. Ghermandi and M. Sinclair, “Passive crowdsourcing of social media in environmental research: A systematic map,” Glob. Environ. Chang., vol. 55, no. January, pp. 36–47, 2019, doi: 10.1016/j.gloenvcha.2019.02.003. [Google Scholar]
  7. P. M. Landwehr, W. Wei, M. Kowalchuck, and K. M. Carley, “Using tweets to support disaster planning, warning and response,” Saf. Sci., vol. 90, pp. 33–47, 2016, doi: 10.1016/j.ssci.2016.04.012. [Google Scholar]
  8. E. Subowo, I. Rosyadi, and H. H. Kusumawardhani, “Twitter Data as Decision Tree Parameter for Analysis of Tourism Potential Policies,” vol. 436, pp. 474–478, 2020, doi: 10.2991/assehr.k.200529.099. [Google Scholar]
  9. E. Subowo, E. Sediyono, and Farikhin, “Ant Colony Algorithm for Determining Dynamic Travel Routes Based on Traffic Information from Twitter,” E3S Web Conf., vol. 125, no. 201 9, 2019, doi: 10.1051/e3sconf/201912523012. [Google Scholar]
  10. A. Krouska, C. Troussas, and M. Virvou, “The effect of preprocessing techniques on Twitter sentiment analysis,” IISA 2016 - 7th Int. Conf. Information, Intell. Syst. Appl., no. July, 2016, doi: 10.1109/IISA.2016.7785373. [Google Scholar]
  11. K. W. Church, “Emerging Trends: Word2Vec,” Nat. Lang. Eng., vol. 23, no. 1, pp. 155–162, 2017, doi: 10.1017/S1351324916000334. [Google Scholar]
  12. R. P. Nawangsari, R. Kusumaningrum, and A. Wibowo, “Word2vec for Indonesian sentiment analysis towards hotel reviews: An evaluation study,” Procedia Comput. Sci., vol. 157, pp. 360–366, 2019, doi: 10.1016/j.procs.2019.08.178. [Google Scholar]
  13. B. Jang, I. Kim, and J. W. Kim, “Word2vec convolutional neural networks for classification of news articles and tweets,” PLoS One, vol. 14, no. 8, pp. 1–20, 2019, doi: 10.1371/journal.pone.0220976. [Google Scholar]
  14. T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 1st Int. Conf. Learn. Represent. ICLR 2013 - Work. Track Proc., pp. 1–12, 2013. [Google Scholar]
  15. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” Adv. Neural Inf. Process. Syst., pp. 1–9, 2013. [Google Scholar]
  16. M. G. Ozsoy, “From Word Embeddings to Item Recommendation,” 2016, [Online]. Available: http://arxiv.org/abs/1601.01356. [Google Scholar]
  17. A. S. Lhoussain, G. Hicham, and Y. Abdellah, “Adaptating the levenshtein distance to contextual spelling correction,” Int. J. Comput. Sci. Appl., vol. 12, no. 1, pp. 127–133, 2015. [Google Scholar]
  18. A. A. Sorokin and T. O. Shavrina, “Automatic spelling correction for Russian social media texts,” Komp’juternaja Lingvistika i Intellektual’nye Tehnol., pp. 688–701, 2016. [Google Scholar]

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