EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 317 (2021) E3S Web Conf., 317 (2021) 05020 References
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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.