Effective Communication and Visualization Techniques for Analyzing Textual Data
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Kellie Keeling
University of Denver
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Word/Phrase Frequencies
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Collocations (Phrases)
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Clustering
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Textual Data contains a large amount of information.
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As you do your analysis, think of ways to use
graphs/visualizations to help your reader easily
absorb your textual data.
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Consider filtering for higher frequencies
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Make sure to 'clean' the data (remove stopwords) to
focus on the important words/terms and combine
similar terms: Statistic & Statistics
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Thank You! Kellie.Keeling@du.edu
1:30 PM Saturday, Explain What It Means: Communication, Visualization, Presentation and Storytelling for Analysis Results, Location: Ballard
Abstract: Discussion of approaches to and importance of incorporating communication in analytical courses. Whether through written analysis, creating presentations, data visualization, or storytelling, the practical relevance of any analysis is best realized when the analysis is communicated, understood, and turned into actionable information. Attendees are encouraged to contribute to the discussion.
Session chair: Linda Boardman Liu, Boston College
Presenters: Linda Boardman Liu, Boston College
Satish Nargundkar, Georgia State University
Wilma Andrews, Virginia Commonwealth University
Kellie Keeling, University of Denver
Wilma Andrews
Virginia Commonwealth University
Trends in Presentations Communicating Results
Satish Nargundkar
Georgia State University
Visual Communication Meaningful Summarization
Linda Boardman Liu
Boston College
Writing in Statistics
Kelly Keeling
University of Denver
Visualization of Textual Data Storytelling
15 min presentations, 30 min discussion
Send references to Linda Ahead of Time
Wilma – digital literacy, passion is presentations (helping a colleague by presenting in communication classes). Trends/what not to do, how to present more than just bullet points – what came up with and not just how they did the analysis
Satish
Specifics about Tables/Charts – Quantitative Data
Satish – teaching data mining/analytics, consulting: how to write a report in 'English' = focusing on highlighting the insights (design tables/charts and graphs). Data mining: how present a logistic regression, other predictive analytics techniques
Linda
Written communication in Statistics courses – write 3 sentences that communicates your ideas, handout turning a regular assignment into a written assignment
Kellie
Text Analytics
Cluster Analysis
Network Diagrams
Frequency Displays (Word Clouds, Heat Maps, Bar Charts)
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.
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Presentation Transcript
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
Department of Business Information & Analytics Department of Business Information & Analytics
Department of Business Information & Analytics Department of Business Information & Analytics
Department of Business Information & Analytics Department of Business Information & Analytics
Visualizations for these Analyses Word/Phrase Frequencies Collocations (Phrases) Clustering Department of Business Information & Analytics Department of Business Information & Analytics
Natural Language Processing Department of Business Information & Analytics Department of Business Information & Analytics
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
Natural Language Processing Department of Business Information & Analytics Department of Business Information & Analytics
Word Frequencies Tableau R SPSS Modeler Department of Business Information & Analytics Department of Business Information & Analytics
Tweets Excel PowerPivot I(http://tinyurl.com/AnalyticsforTwitter) Department of Business Information & Analytics Department of Business Information & Analytics
Word Cloud R Department of Business Information & Analytics Department of Business Information & Analytics
Filter (Freq > 8) Tableau R Department of Business Information & Analytics Department of Business Information & Analytics
Heat Map Tableau Department of Business Information & Analytics Department of Business Information & Analytics
Heat Map (Filter Freq > 8) Tableau Department of Business Information & Analytics Department of Business Information & Analytics
Parts of Speech Excel Pivot Tables/Pie NN: Noun, singular, NNP: Proper Noun, NNS: Noun, plural, JJ: Adjective, RB: Adverb
Collocations: Bigrams 2008 Bigrams occur more than twice Excel Node XL
Term by Document Matrix Documents Department of Business Information & Analytics Department of Business Information & Analytics
Clustering (k Means) R
Clustering (SPSS Modeler) (Shows 1 cluster and dotted lines to external dvi port)
Clustering (Hierarchical) R
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
