Connecting Social Media Data with Observed Hybrid Data for Environment Monitoring

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 737)

Abstract

Environmental monitoring has been regarded as one of effective solutions to protect our living places from potential risks. Traditional methods rely on periodically recording assessments of observed objects, which results in large amount of hybrid data sets. Additionally public opinions regarding certain topics can be extracted from social media and used as another source of descriptive data. In this work, we investigate how to connect and process the public opinions from social media with hybrid observation records. Particularly, we study Twitter posts from designated region with respect to specific topics, such as marine environmental activities. Sentiment analysis on tweets is performed to reflect public opinions on the environmental topics. Additionally two hybrid data sets have been considered. To process these data we use Hadoop cluster and utilize NoSql and relational databases to store data distributed across nodes in share nothing architecture. We compare the public sentiments in social media with scientific observations in real time and show that the “citizen science” enhanced with real time analytics can provide avenue to nominatively monitor natural environments. The approach presented in this paper provides an innovative method to monitor environment with the power of social media analysis and distributed computing.

Keywords

Social media Sentiment analysis Hybrid data 

Notes

Acknowledgements

This project was funded through a National Environment Science Program (NESP) fund, within the Tropical Water Quality Hub (Project No: 2.3.2). We would also like to thank the Great Barrier Reef Marine Park Authority and CoralWatch for providing citizen science data for the purpose of this research.

References

  1. 1.
    Bjorkelund, E., Burnett, T., Norvag, K.: A study of opinion mining and visualization of hotel reviews. In: Proceedings of the 14th International Conference on Information Integration and Web-based Applications and Services, pp. 229–238Google Scholar
  2. 2.
    Brob, J.: Aspect-oriented sentiment analysis of customer reviews using distant supervision techniques. Ph.D. Thesis, Department of Mathematics and Computer Science, University of BerlinGoogle Scholar
  3. 3.
    Claster, W., Dinh, Q., Cooper, M.: Naive bayes and unsupervised artificial neural nets for caneun tourism social media data analysis. In: Nature and Biologically Inspired Computing, in Proceedings of the Second World Congress on Nature and Biologically Inspired Computing, pp. 158–163Google Scholar
  4. 4.
    Claster, W., Dinh, Q., Cooper, M.: Thailand-tourism and conflict modelling sentiment from twitter tweets using nave bayes and unsupervised artificial neural nets. In: Proceedings of the second International Conference on Computational Intelligence, Modelling and Simulation, pp. 89–94Google Scholar
  5. 5.
    Franciscus, N., Milosevic, Z., Stantic, B.: Influence of parallelism property of streaming engines on their performance. In: ADBIS (Short Papers and Workshops) Communications in Computer and Information Science, vol. 637, pp. 104–111. Springer (2016)Google Scholar
  6. 6.
    Hutto, C., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the 8th International AAAI Conference on Weblogs and Social MediaGoogle Scholar
  7. 7.
    Kasper, W., Vela, M.: Sentiment analysis for hotel reviews. In: Proceedings of the Computational Linguistics-Applications Conference, pp. 45–52Google Scholar
  8. 8.
    Meo, P., Messina, F., Rosaci, D., Sarne, M.: Combining trust and skills evaluation to form e-learning classes in online social networks. Inf. Sci. 405, 107–122 (2017)CrossRefGoogle Scholar
  9. 9.
    Ribeiro, F., Araujo, M., Goncalves, P., Goncalves, M.F.B.: A benchmark comparison of state-of-the-practice sentiment analysis methods (2015). arXiv151201818N
  10. 10.
    Sharma, D., Kulshreshtha, A., P. PaygudeShahrivari, S.: Tourview: sentiment based analysis on tourist domain. Int. J. Comput. Sci. Inf. Technol. 6(3), 2318–2320 (2015)Google Scholar
  11. 11.
    Shi, H., Li, X.: A sentiment analysis model for hotel reviews based on supervised learning. In: International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, China, July 10–13, 2011, Proceedings, pp. 950–954 (2011)Google Scholar
  12. 12.
    Stantic, B., Pokornỳ, J.: Opportunities in big data management and processing. Front. Artif. Intell. Appl. 270, 15–26 (2014). IOS PressGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Griffith Institute for TourismBrisbaneAustralia
  2. 2.Institute for Integrated and Intelligent SystemsGriffith UniversityBrisbaneAustralia

Personalised recommendations