Emotional Web Intelligence

I was always interested how it is possible to analyse non-cognitive side of human interactions. What i have done in my master thesis was the term frequency of categories words for a thread in a mailing list. The categories was kindly presented from James W. Pennebaker. He and his colleagues did a great job on creating the repositiry of the words that are classified according to the emotions/feelings people try to express in their writing. They use the repository now in their text analysis software.

A community of a mailing list was characterized by an emotional vector as well as by a structural vector. I examined all communities vectors in order to find similar communities. The results for clusters based on structure and clusters based on emotional analysis were different.

Analysing all the results i suppose that the situation we can see in a network structure is not correlating with the emotional preferences. Human interactions depend on many other different factors like prestige (created through a network structure), duties (put by a organizational structure) and many others.

We have to communicate to somebody we don't like to. But will such a communication be successful, efficient and enduring?

The human interactions can be perfectly visualized with the help of graphs:


and can be structurally analysed:


In our example the node 25 and the node 60 are not interacting, though they produce and consume the correlating number of words. However, those words possibly are produced by the node 26 who is between those nodes. Concerning to the other nodes, it is no evident correlation in characteristics was discovered. Anyway, if we switch to the node 36 characteristics we can’t find any value that will correlate with the cognitive characteristics of the nodes. The node 36 has no edges with the others and that might be a reason. Nevertheless, according to numerous experiments we could not prove the assumption that the interacting with each other members possess correlating or non-correlating emotional vectors.

Anyway emotions can be used during graph visualization so that it will be clear who is connected with whom non-cognitively(by colors, or by positions in a graph).

I would like to follow the community evolution and its dependency on emotions. Moreover it is useful to focus and discover other dependencies that influences on sucess or failure of communities.