Tag Archives: #fote11

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[This text originally appeared in Live Twitter data from FOTE #fote11 post but I’m extracting here to provide a separate space for comment (and hit RSS aggregators with something I think is quite interesting]

fote11-all-day-sentiment_thumb[1]

Using a combination of my Using Google Spreadsheets as a data source to analyse extended Twitter conversations in NodeXL (and Gephi) and Using the Viralheat Sentiment API and a Google Spreadsheet of conference tweets to find out how that keynote went down I was able to import sentiment data for each tweet (edge). I then accumulated the sentiment probability for each twitterer (vertex), -1 = 100% probability of negative sentiment +1 = 100% probability of positive sentiment, and averaged the overall sentiment by the number of tweets that person (vertex) made. Using the autofill the vertices were coloured by average sentiment probability (green = +ve, red = –ve) and grouped by overall positive sentiment, overall negative sentiment and no data.

The graph shows that over 80% (n.296) of #fote11 hash taggers (n.355) posted tweets with an overall positive sentiment detection.

Somethings worth noting with this data. Sentiment analysis is being analysed using machine detection (ie it might be wrong). Someone with overall negative sentiment doesn’t necessarily indicate that they had a bad event experience. If the person was reflecting on issues being presented or quoting others who had a negative experience this will be reflected in their sentiment score. The bottom line is the graph gives an overview of a more complex story. If you want to start unpicking that story yourself the GraphML data is available on the NodeXL GraphGallery.

Update: Just so you don’t think @jamsclay is the ‘king of miserable’ these were the tweets detected as negative sentiment  … I’ll let you decide ;)

Not at FOTE11 today (you can follow the live stream) but was interested in what data I could extract from the #fote11 twitter stream and process as the event unfolds. Most of the data here comes from a mashup of my Google Spreadsheet mashup. (This is perhaps putting Google Spreadsheets to the limit in terms of fetching data but lets see how it goes)

Update: Some of these graphs may take a couple of seconds to appear [H/T @johnmclear]

Update 2: The interactive #fote community was freezing Internet Explorer so click through to see interactive version [H/T James Swansburg]

Update 3: Added a second snapshot of the Twitter conversations to cover whole day

Update 4: Coded the last snapshot with sentiment data from ViralHeat

Summary Data

Guages

Archive of Tweets

Here's a archive of the tweets from the event captured in a Google Spreadsheet.

Sentiment

Open in new window

#fote11 community

#fote11 community [click for interactive version]

[The interactive version was freezing Internet Explorer so click through to see interactive version H/T James Swansburg]

#fote11 conversations (made with NodeXL)

Snapshot below taken at midday
Node size = number of tweets with #fote11
Graphing 1622 tweets

fote11 conversation network 1622 tweets

Snapshot below taken post event
Node size = number of tweets with #fote11
Graphing 2649 tweets

fote11 conversation network 2649 tweets

#fote11 conversations coded with sentiment data from ViralHeat

#fote11 Twitter conversations coded with sentiment data from ViralHeat

Last graph for now. Using a combination of my Using Google Spreadsheets as a data source to analyse extended Twitter conversations in NodeXL (and Gephi) and Using the Viralheat Sentiment API and a Google Spreadsheet of conference tweets to find out how that keynote went down I was able to import sentiment data for each tweet (edge). I then accumulated the sentiment probability for each twitterer (vertex), -1 = 100% probability of negative sentiment +1 = 100% probability of positive sentiment, and averaged the overall sentiment by the number of tweets that person (vertex) made. Using the autofill the vertices were coloured by average sentiment probability (green = +ve, red = –ve) and grouped by overall positive sentiment, overall negative sentiment and no data.

The graph shows that over 80% (n.296) of #fote11 hash taggers (n.355) posted tweets with an overall positive sentiment detection.

Somethings worth noting with this data. Sentiment analysis is being analysed using machine detection (ie it might be wrong). Someone with overall negative sentiment doesn’t necessarily indicate that they had a bad event experience. If the person was reflecting on issues being presented or quoting others who had a negative experience this will be reflected in their sentiment score. The bottom line is the graph gives an overview of a more complex story. If you want to start unpicking that story yourself the GraphML data is available on the NodeXL GraphGallery.