Predictive Analytics and Politics – Part 2

We make the assumption that in the text representation shown above …

We make the assumption that in the
text representation shown above Cluster 5 (which contains 329 citizen
messages) is about requests for a fair tax plan and Cluster 10 contains
messages with requests that tax fraud should be minimized. It appears
that significantly less people are concerned with a battle against
fraudulent activity but they request -more immediate- benefits through a
fair tax plan.

Collecting and analyzing information found
in blogs and forum entries is another area of analysis that could prove
very interesting. Let’s see an example with the Political / Social /
Economic situation in Greece : The goal is to identify and extract
trends and co-occurences of key concepts from blog titles and forum
posts such as :

For
this kind of data several applications can emerge. We could track
specific concepts through time and see their trends. We can also
identify which concepts are discussed together. As an example we could
identify the reasons on why Giorgos Papandreou (PM of Greece) is
characterized in a negative way in blog posts. (= what other concepts are
found in Blog posts containing keywords ‘Giorgos Papandreou’ AND Bad
Characterizations?) :

(Note : PASOK = Governmental Political Party )

Politics
= 120

Economy=72

Economy, Politics=40

PASOK=24

Politics, PASOK, Referendum=8

Economy, Politics,PASOK,Referendum, Immigrants=8

Economy, Politics, Society=8

Society, PASOK=4

In other words : Giorgos Papandreou is
criticized mainly for his Political decisions and the Economy followed
by criticism on PASOK. Negative sentiment also exists because of the
fact that a percentage of Greek citizens require that a referendum
should take place concerning the latest decision of the Greek government
to give to a large proportion of Immigrants the Greek citizenship.