Introducing Students to the Analysis of Text Data Increase Relevance by Shifting Focus Away from Classical Statistical Mechanics & Hypothesis Testing Making.

Slides:

Advertisements

Similar presentations

Frayer Model Definition Characteristics Concept Examples Non-Examples.

Advertisements

The Central Idea of Informative Text

The Craft of Revision Inside of a Personal Narrative

Misleading Metrics and Unsound Analyses Presenter: Gil Hartman Authors: Barbara Kitchenham, David Ross Jeffery, and Colin Connaughton IEEE Software 24(2),

TPCASTT Group Breakdown

Business and Management Research WELCOME. Lecture 9.

Lesson 4 Reading as a Writer. “If you don't have time to read, you don't have the time (or the tools) to write. Simple as that.” ― Stephen KingStephen.

Introduction to Data Analysis *Training Session*

Comprehension Strategy Routine Cards

 The Shift Towards Digital Branding By Zahra Karim.

REVIEW is GOOD  Organization  Plan, Prioritize  Daily Chores REVIEW.

Data Management Grade 7. What’s the Story? Secondary data is information that was collected by someone else. Referring to information that was published.

Active Engagement In Lecture Person A states his/her understanding of what has been said, then elaborates. Person B agrees or disagrees with the interpretation,

K-2 ELA Academy: Part 4 Progress Monitoring and Goal Setting.

Page 1 Graphical Analysis of Motion By: Mike Maloney.

Jan Stallaert Professor OPIM

Method Type in your method here. Describe participants, sampling, variables. Type in your method here. Describe participants, sampling, variables. Type.

Abstract Type in your abstract here. This is 36 point font. This is a brief comprehensive summary. Type in your abstract here. This is a brief comprehensive.

Statistics: Unlocking the Power of Data Lock 5 Afternoon Session Using Lock5 Statistics: Unlocking the Power of Data Patti Frazer Lock University of Kentucky.

+ The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE Chapter 1: Exploring Data Introduction Data Analysis: Making Sense of Data.

SASIMI2015 Short Presentation Slide Example Tai-Chen Chen, National Central University R0-0 Put the summary of the problem, your solution, key idea, contribution,

Engineering Practicum Baltimore Polytechnic Institute M. Scott Lab Reports Objective: Understand and be able to meet lab report requirements and expectations.

Prewriting.  This is a prewriting strategy will take students through three levels of scaffolding.  This strategy addresses:  analyzing information.

Scientific Communication

Science Question of the Day Do you think you like Science class? Why or why not?

Raising the Quality of Narrative Writing Advanced Writing Workshop.

ENGLISH II SEPTEMBER 26, 2011 Turn in worksheet for “And of Clay Are We Created” now. No warm-up today. Right now, get out a piece of paper to take notes.

Please begin filling out the student interest survey while you wait.

 Unit 4 ~ Composition.  Time! Time to complete the lessons on the OLS Writing in action Level C book Pencil paper A book to review.

Do Now I would like you to answer the question for the Activating Strategy on page one of the Scientific Method packet. Do this by yourself 1.

Scientific Method Lesson 2 1 Scientist _________________________________.

Wiki Workshop for UHL 2332 Academic Report Writing.

Scientific Method A blueprint for experiment success.

1 Doing Statistics for Business Doing Statistics for Business Data, Inference, and Decision Making Marilyn K. Pelosi Theresa M. Sandifer Chapter 12 Multiple.

Reading STAAR Strategies

EMPOWER TOOLS FOR OUR TOOLBELT ***IMPORTANT*** If a tool doesn’t work, don’t keep using it! Also if you don’t feel comfortable or safe using one of the.

Common Core Historical Fiction Lesson Goal: We will make larger interpretations of our text while understanding several different global lessons (theme),

QM Spring 2002 Business Statistics Bivariate Analyses for Qualitative Data.

Exploring the Literacy Standards: CCSS & Main Idea.

Scatterplots & Correlations Chapter 4. What we are going to cover Explanatory (Independent) and Response (Dependent) variables Displaying relationships.

Do not alter this template – move logos, change color or format

Chapter 1: Exploring Data

university of connecticut school of nursing

Computing and Data Analysis

Chapter 1: Exploring Data

university of connecticut school of nursing

Do not alter this template – move logos, change color or format

Do not alter this template – move logos, change color or format

university of connecticut school of nursing

university of connecticut school of nursing

Subject Vs. Theme.

Chapter 1: Exploring Data

Descriptive vs. Inferential

Statistics Applied to Economics I

Do not alter this template – move logos, change color or format

TITLE OF PRESENTATION PRESENTER.

Do not alter this template – move logos, change color or format

Do not alter this template – move logos, change color or format

Chapter 1: Exploring Data

Chapter 1: Exploring Data

Do not alter this template – move logos, change color or format

Chapter 1: Exploring Data

Chapter 1: Exploring Data

Chapter 1: Exploring Data

Chapter 1: Exploring Data

Do not alter this template – move logos, change color or format

Title Introduction: Discussion & Conclusion: Methods & Results:

Chapter 1: Exploring Data

university of connecticut school of nursing

Presentation transcript:

Introducing Students to the Analysis of Text Data Increase Relevance by Shifting Focus Away from Classical Statistical Mechanics & Hypothesis Testing Making Statistics More Effective in Schools of Business Mini-Conference Kellie B. Keeling Business Information & Analytics, University of Denver

An Example of Text Analysis Twitter Data Analyze the Tweets from Panera Bread and Compare Tweets from McDonalds and Burger King I used this free tool to pull down tweets (uses Power Pivot) : Analytics for Twitter.xlsx First Portion: Use Pivot Tables to Summarize Tweets from Panera Bread Second Portion: Discuss Text Analytic terms (stop words, valence/tone) and compare dashboards of McDonalds and Burger King Tweets

Panera Tweets Data Retweet Tweeter Tone Score/Tone Hashtag Date/Hour/Time of Day Author Title (Text)

Panera Tweets Data Had them create 4 Pivot Charts from the data Discuss what Panera Bread can learn from their Tweets based on your charts. Goal: See how you can turn “text” data into something quantitative that can be “analyzed.

McDonalds Vs. Burger King Tweets Goal: Discuss Text Analytic terms “stop words” and, “valence/tone” see ways this data can be displayed and interpreted.

Sample from Burger King!

Sample from McDonalds

Tone Dictionary

Tone/Valence For the page with the dashboard of charts, the Tone is noted as either “Neutral”, “Positive”, or “Negative.” To rate the tweets, the Tone Dictionary on the next page was used. Using the list of sample tweets, what other words do you think should be added? POSITIVENEGATIVE

Tone/Valence What are some words that aren’t in these tweets that you think should be added to the Tone dictionary? POSITIVENEGATIVE

Stop Words When a computer program or software tries to determine the meaning of a “tweet,” it uses a set of “stop words” that the computer skips over when interpreting the meaning. Fill in possible Stop Words: STOP WORDS a an the

Dashboard of Tweets

Dashboard Summary Which company has more tweets? What time of day does each company have the most tweets? What can you say about the tone of the tweets over the time period? C omment on the number of retweets versus tweets by company. Summarize what you have learned that you could tell an executive at Burger King or McDonalds.

Conclusion I feel this is a great way to introduce the ideas of text analysis. Worked very well as tool to introduce text analytics to middle school girls! I plan to create a lesson similar to this for my “Statistics II” course (probability through simple regression) starting in January.