FlowingData: How to Make an Interactive Network Visualization

I have looked at d3 network visualization demos / tutorials before. I always tell myself I will do it. But I need to get on this. Link below is to a tutorial on flowing data. https://www.google.com/producer/editions/CAowtpoM/flowingdata/CAIiEP1jg1drOnN8PC3TcT9b0fUqMggEIhDlFy4k5ajnTsfC5QXSbl75KhwIACIQ_IySbXiwavHpyb8VqOTmZCoGCAowtpoM/how_to_make_an_interactive_netwo

Many Eyes : Survey on Income and Living Conditions in Ireland 2010

Visualizations : Survey on Income and Living Conditions in Ireland 2010

I bought the book visualize this written by Nathan Yau creator of the website Flowingdata (one of my favorite data visualization websites).

Below is one of the coolest tools I found on the program Many Eyes, I was playing around trying to program a tool like this. I knew options existed. But Many Eyes just does it so elegantly.

via Many Eyes : Survey on Income and Living Conditions in Ireland 2010.

Word Co-occurrence

Lets say we have a database table called responses, each row contains a word.

responses Table:

id positive response
1 true I have a great experience. I was treated very well. The person was very nice
2 false I had a terrible experience. I was not treated very well. I thought person was very mean.

We map give each word an id on one table. Lets call it the words table.

words Table:

id positive word count
1 true experience 1
2 false experience 1
3 true I 2
4 false I 3

We go to each row, we get all the words, if the word does not exist in the words table we add the word. (A new id will be created associated with that word)

Then we get every combination of 2 words in that paragraph and add it to a occurrences table.

occurrences Table:

word1_id word2_id count
1 3 2
2 4 3

Question: Do we want to count the same word in the same sentence more than once in relationships? The word ‘I’ and ‘experience’ occur three times together in that second sentence?

Basically we than get all the true occurrences and rank them by count. Same with false occurrences and we can present them however we want.
Blogs / Pictures (of what I might want)

High Resolution Maps of Science

Algorithms extracting linguistic relations and their evaluation

Text Algorithms 

Text Mining

Rapid Miner – open source data mining, java based, has filtering options.

Kind of Related But Very Interesting

Visual Thesaurus – We could do something similar to this but you also pick a minimum threshold and it shows all the word related that meet it.