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Classifying Twitter Content

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Stephen Dann
Stephen Dann
TecnologíaEmpresariales
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Classifying Twitter Content Dr Stephen Dann Australian National University @stephendann Presented at Marketing Science, Houston, June 11, 2011
If you’re on Twitter Questions can be sent to @stephendann or Hashtag #mktsci2011
Why here, why now? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Series of Projects ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Twitter. ,[object Object],[object Object],Twitter in Plain English
How to analyze a living medium? Hawthorn Effect*Uncertainty Principle Sample Size / Twitter Volume [ ]
Why do any coding? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Raw Counts Tweetstats – www.tweetstats.com
Text Analysis Tweetstats – www.tweetstats.com Wordle – wordle.com
Prior Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dann (2010) based on:
Schema ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Conversational ,[object Object],[object Object],[object Object],[object Object]
News Events ,[object Object],[object Object],[object Object],[object Object]
Pass along ,[object Object],[object Object],[object Object],[object Object]
Phatic ,[object Object],[object Object],[object Object],[object Object]
Status ,[object Object],[object Object],[object Object],[object Object]
Sub categories ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sub categories ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Sub categories ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Marketing Science Style ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
1072 1823 4344 1020 602 2811 11672   Total 31 34 126 153 10 834 1188 10% Status 20 24 69 60 12 213 398 3% Phatic 896 949 2780 351 533 278 5787 50% Pass Along 10 31 784 29 17 13 884 8% News Events 115 785 585 427 30 1473 3415 29% Convers-ational Ener. Counc. Police #Conf #Dis. Dann n Data  
Uses of the Data
Here’s where you come in…
The Challenge Time Day Month Year * Spam gets a category indicated as “Delete” 140 characters of text [C] [S] [PA] [N] [P] [X]* [C 1 ] [C 2 ] [C 3 ] [C 4 ] [S 1 ] [S 2 ] [S 3 ] [S 4 ] [S 5 ] [S 6 ] [S 7 ] [S 7 ] [PA 1 ] [PA 2 ] [PA 3 ] [PA 4 ] [PA 5 ] [N 1 ] [N 2 ] [N 3 ] [N 4 ] [N 5 ] [N 6 ] [N 7 ] [P 1 ] [P 2 ] [P 3 ] [P 4 ] [X 1 ] [X 2 ] [X 3 ] [X 4 ]
Future plans ,[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object]
Questions [email_address] Or @stephendann
[object Object]

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Classifying Twitter Content

  • 1. Classifying Twitter Content Dr Stephen Dann Australian National University @stephendann Presented at Marketing Science, Houston, June 11, 2011
  • 2. If you’re on Twitter Questions can be sent to @stephendann or Hashtag #mktsci2011
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  • 6. How to analyze a living medium? Hawthorn Effect*Uncertainty Principle Sample Size / Twitter Volume [ ]
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  • 8. Raw Counts Tweetstats – www.tweetstats.com
  • 9. Text Analysis Tweetstats – www.tweetstats.com Wordle – wordle.com
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  • 22. 1072 1823 4344 1020 602 2811 11672   Total 31 34 126 153 10 834 1188 10% Status 20 24 69 60 12 213 398 3% Phatic 896 949 2780 351 533 278 5787 50% Pass Along 10 31 784 29 17 13 884 8% News Events 115 785 585 427 30 1473 3415 29% Convers-ational Ener. Counc. Police #Conf #Dis. Dann n Data  
  • 23. Uses of the Data
  • 24. Here’s where you come in…
  • 25. The Challenge Time Day Month Year * Spam gets a category indicated as “Delete” 140 characters of text [C] [S] [PA] [N] [P] [X]* [C 1 ] [C 2 ] [C 3 ] [C 4 ] [S 1 ] [S 2 ] [S 3 ] [S 4 ] [S 5 ] [S 6 ] [S 7 ] [S 7 ] [PA 1 ] [PA 2 ] [PA 3 ] [PA 4 ] [PA 5 ] [N 1 ] [N 2 ] [N 3 ] [N 4 ] [N 5 ] [N 6 ] [N 7 ] [P 1 ] [P 2 ] [P 3 ] [P 4 ] [X 1 ] [X 2 ] [X 3 ] [X 4 ]
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