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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
3.
4.
5.
6.
How to analyze
a living medium? Hawthorn Effect*Uncertainty Principle Sample Size / Twitter Volume [ ]
7.
8.
Raw Counts Tweetstats
– www.tweetstats.com
9.
Text Analysis Tweetstats
– www.tweetstats.com Wordle – wordle.com
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
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 ]
26.
27.
28.
29.
30.
31.
Questions [email_address] Or
@stephendann
32.
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