[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]
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 😉