Effective Communication and Visualization Techniques for Analyzing Textual Data

Enhance your data analysis skills with effective communication, visualization, presentation, and storytelling techniques. Discover how to analyze textual data through word/phrase frequencies, collocations, and clustering. Explore tools for text processing and natural language processing, such as Excel, SPSS Modeler, Python, and more, to extract valuable insights. Dive into the world of data analytics with practical examples and case studies from the Department of Business Information & Analytics at the University of Denver.

• Data analysis
• Visualization techniques
• Textual data
• Communication skills
• Natural language processing

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1. MSMESB: Explain What It Means: Communication, Visualization, Presentation and Storytelling for Analysis Results Visualization of Textual Data Storytelling Kellie Keeling University of Denver Department of Business Information & Analytics

2. Department of Business Information & Analytics Department of Business Information & Analytics

3. Department of Business Information & Analytics Department of Business Information & Analytics

4. Department of Business Information & Analytics Department of Business Information & Analytics

5. Visualizations for these Analyses Word/Phrase Frequencies Collocations (Phrases) Clustering Department of Business Information & Analytics Department of Business Information & Analytics

6. Natural Language Processing Department of Business Information & Analytics Department of Business Information & Analytics

7. Software that helps Process Text Excel (manual process) Commercial Products (student options) SPSS Modeler Text Analytics SAS Enterprise Miner Alteryx (GUI for R) Open Source Coding Languages Python R Weka (GUI/Coding) Department of Business Information & Analytics Department of Business Information & Analytics

8. Natural Language Processing Department of Business Information & Analytics Department of Business Information & Analytics

9. Word Frequencies Tableau R SPSS Modeler Department of Business Information & Analytics Department of Business Information & Analytics

10. Tweets Excel PowerPivot I(http://tinyurl.com/AnalyticsforTwitter) Department of Business Information & Analytics Department of Business Information & Analytics

11. Word Cloud R Department of Business Information & Analytics Department of Business Information & Analytics

12. Filter (Freq > 8) Tableau R Department of Business Information & Analytics Department of Business Information & Analytics

13. Heat Map Tableau Department of Business Information & Analytics Department of Business Information & Analytics

14. Heat Map (Filter Freq > 8) Tableau Department of Business Information & Analytics Department of Business Information & Analytics

15. Parts of Speech Excel Pivot Tables/Pie NN: Noun, singular, NNP: Proper Noun, NNS: Noun, plural, JJ: Adjective, RB: Adverb

16. Collocations: Bigrams 2008 Bigrams occur more than twice Excel Node XL

17. Term by Document Matrix Documents Department of Business Information & Analytics Department of Business Information & Analytics

18. Clustering (k Means) R

19. Clustering (SPSS Modeler) (Shows 1 cluster and dotted lines to external dvi port)

20. Clustering (Hierarchical) R

21. Conclusions Textual Data contains a large amount of information. As you do your analysis, think of ways to use graphs/visualizations to help your reader easily absorb your textual data. Consider filtering for higher frequencies Make sure to 'clean' the data (remove stopwords) to focus on the important words/terms and combine similar terms: Statistic & Statistics Thank You! Kellie.Keeling@du.edu Department of Business Information & Analytics Department of Business Information & Analytics