FEATURES OF BIG TEXT DATA VISUALIZATION FOR MANAGERIAL DECISION MAKING
Abstract and keywords
Abstract (English):
This paper describes text data analysis in the course of managerial decision making. The process of collecting textual data for further analysis as well as the use of visualization in human control over the correctness of data collection is considered in depth. An algorithm modification for creating an "n-gram cloud" visualization is proposed, which can help to make visualization accessible to people with visual impairments. Also, a method of visualization of n-gram vector representation models (word embedding) is proposed. On the basis of the conducted research, a part of a software package was implemented, which is responsible for creating interactive visualizations in a browser and interoperating with them.

Keywords:
visualization, natural language processing, web application accessibility
References

1. Accessible Colors for Data Visualization. Available bylink: https://medium.com/@zachgrosser/accessible-colorsfor-data-visualization-2ad64ac4ee7e

2. Causes of Colour Blindness. Available by link:http://www.colourblindawareness.org/colourblindness/causes-of-colour-blindness/

3. CSS Grid - Table layout is back. Be there and be square.Available by link:https://developers.google.com/web/updates/2017/01/cssgrid

4. Kaser O., Lemire D. (2007). Tag-Cloud Drawing:Algorithms for Cloud Visualization. Tagging and Metadatafor Social Information Organization. A workshop atWWW2007, pp 1086-1087.

5. KPMG presents the results of a survey of Russia's mergersand acquisitions market in 2017. Available by link:https://home.kpmg/ru/en/home/media/pressreleases/2018/03/ma-survey-2017.html

6. Kutuzov A, Kutuzov I. (2015) Texts in, meaning out: neurallanguage models in semantic similarity task for Russian.Proceedings of the Dialog 2015 Conference, Moscow,Russia

7. Mai F., Mai T., Ling C., Ling M. (2018). Deep LearningModels for Bankruptcy Prediction using TextualDisclosures. European Journal of Operational Research.doi:https://doi.org/10.1016/j.ejor.2018.10.024.

8. Make your information more accessible. NationalDisability Authority. Available bylink:http://nda.ie/Resources/Accessibility-toolkit/Makeyour-information-more-accessible/

9. Podvesovskii A.G., Isaev R.A. (2018) VisualizationMetaphors for Fuzzy Cognitive Maps. ScientificVisualization, vol. 10, no. 4, pp. 13-29. doihttps://doi.org/10.26583/sv.10.4.02

10. Podvesovskii A.G., Gulakov K.V., Dergachyov K.V.,Korostelyov D.A., Lagerev D.G. (2015) The choice ofparameters of welding materials on the basis of fuzzycognitive model with neural network identification ofnonlinear dependence. Proceedings of the 2015International Conference on Mechanical Engineering,Automation and Control Systems (MEACS) (Tomsk,Russia, December 1-4, 2015), IEEE Catalog Number:CFP1561Y-ART, pp. 02-38-NSAP.doi:https://doi.org/10.1109/MEACS.2015.741490

11. Prangnawarat N., Hulpus I., Hayes C. (2015) EventAnalysisin Social Media using Clustering ofHeterogeneous Information Networks. The 28thInternational FLAIRS Conference (AAAI Publications)(AAAI)

12. Staff turnover has started to grow. Available by link:https://www.antalrussia.com/news/staff-turnover-hasstarted-to-grow/

13. The canvas elements. Available by link:https://html.spec.whatwg.org/multipage/canvas.html#thecanvas-element

14. The Future of Data Visualization: Predictions for 2019 andBeyond A. Available by link:https://depictdatastudio.com/the-future-of-datavisualization-predictions-for-2019-and-beyond/

15. Viégas B., Wattenberg M., Feinberg J. (2009) Participatoryvisualization with Wordle. IEEE Transactions onVisualization and Computer Graphics 15, no. 6, pp. 1137-1144. doihttps://doi.org/10.1109/TVCG.2009.17

16. Web Content Accessibility Guidelines (WCAG) 2.1.Available by link: https://www.w3.org/TR/WCAG21/

17. Zakharova A.A., Lagerev D.G., Makarova E.A. (2019)Evaluation of the semantic value of textual information forthe development of management decisions. CPT2019 TheConference Proceedings, TzarGrad, Moscow region,Russia

18. Zakharova A.A., Vekhter E.V., Shklyar A.V. (2017)Methods of Solving Problems of Data Analysis UsingAnalytical Visual Models. Scientific Visualization, vol. 9,no. 4, pp. 78-88. doi:https://doi.org/10.26583/sv.9.4.08

19. Zhao J., Zhao G., Zhao L., Zhao W., (2014). PEARL: AnInteractive Visual Analytic Tool for UnderstandingPersonal Emotion Style Derived from Social Media. IEEEConference on Visual Analytics Science and Technology,VAST 2014 - Proceedings.doi:https://doi.org/10.1109/VAST.2014.7042496.

Login or Create
* Forgot password?