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SPATIAL ANALYSES OF TWITTER DATA CASE STUDIES

Paszto, Vit, Darena, Frantisek, Marek, Lukas, Fuskova, Dana

First published: 2014-06-20https://doi.org/10.5593/sgem2014/b21/s8.101View metrics

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Publication details

Title
SPATIAL ANALYSES OF TWITTER DATA CASE STUDIES
Authors
Paszto, Vit, Darena, Frantisek, Marek, Lukas, Fuskova, Dana
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 14th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING
Publisher
Stef92 Technology
Year
2014
Pages
Not available yet
ISSN
1314-2704
ISBN
978-619-7105-10-0
Language
en
Publication type
Conference Paper
References16

  1. Croitoru, A., et al. (2014) : Geoinformatics and Social Media: A New Big Data Challenge, in Karimi, H. (ed.), Big Data Techniques and Technologies in Geoinformatics. CRC Press, Boca Raton, FL, pp. 207-232

  2. Gottschlich, J., Hinz, O. (2014): A decision support system for stock investment recommendations using collective wisdom. Decision Support Systems 59, 52-62.

  3. Hagenau, M., Liebman, M., Neumann, D. (2013): Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems 55, 685-697.

  4. Mostafa, M. M. (2013): More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications 40 (10), 4241–4251.

  5. Nagar, A., Hashler, M. (2012): Using text and data mining techniques to extract stock market sentiment from live news streams. International Conference on Computer Technology and Science (ICCTS 2012).

  6. Salton, G. (1971): The SMART retrieval system — experiments in automatic document processing. Upper Saddle River, NJ: Prentice-Hall.

  7. Stefanidis, T., Crooks, A.T., Radzikowski, J. (2013): Harvesting ambient geospatial information from social media feeds, GeoJournal, 78(2): 319– 338.

  8. Tumasjan, A., Sprenger, T., Sandner, P., Welpe, I. (2011): Election forecasts with Twitter: How 140 characters reflect the political landscape. Social Science Computer Review 29, 402–418.

  9. Croitoru, A., et al. (2014) : Geoinformatics and Social Media: A New Big Data Challenge, in Karimi, H. (ed.), Big Data Techniques and Technologies in Geoinformatics. CRC Press, Boca Raton, FL, pp. 207-232

  10. Gottschlich, J., Hinz, O. (2014): A decision support system for stock investment recommendations using collective wisdom. Decision Support Systems 59, 52-62.

  11. Hagenau, M., Liebman, M., Neumann, D. (2013): Automated news reading: Stock price prediction based on financial news using context-capturing features. Decision Support Systems 55, 685-697.

  12. Mostafa, M. M. (2013): More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications 40 (10), 4241–4251.

  13. Nagar, A., Hashler, M. (2012): Using text and data mining techniques to extract stock market sentiment from live news streams. International Conference on Computer Technology and Science (ICCTS 2012).

  14. Salton, G. (1971): The SMART retrieval system — experiments in automatic document processing. Upper Saddle River, NJ: Prentice-Hall.

  15. Stefanidis, T., Crooks, A.T., Radzikowski, J. (2013): Harvesting ambient geospatial information from social media feeds, GeoJournal, 78(2): 319– 338.

  16. Tumasjan, A., Sprenger, T., Sandner, P., Welpe, I. (2011): Election forecasts with Twitter: How 140 characters reflect the political landscape. Social Science Computer Review 29, 402–418.

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