My Analytic Research: Eurovision 2013 forecasting using NLP and association rules

About Eurovision 2013 forecasting using NLP: the day after

Today, on the 18th of May, in Sweden, the Eurovision Song Contest 2013 will be held. I tried to make a forecasting of the winners, on the basis of Twitter messages, using natural language processing (NLP), a theory of association rules and semantic fields. I downloaded the tweets with the keywords (eurovision) just for one day - May 17. The analysis conducted shows that the winner is going to be a singer from Denmark, the next three places will go to Ukraine, Russia and Ireland. Well, we'll see :)
If my forecasting is correct, I will write the algorithms of my analysis in detail.

Here are some of obtained association rules and their presentations :

lhs          rhs          support confidence     lift
1 {denmark,
   russia,
   ukraine} => {win}    0.009779951        0.8 1.216357
2 {ireland,
   russia,
   ukraine} => {winner} 0.002444988        1.0 2.655844
3 {denmark,
   ireland,
   ukraine} => {winner} 0.002444988        1.0 2.655844
4 {denmark,
   ireland,
   russia} => {winner} 0.002444988        1.0 2.655844
5 {denmark,
   ireland,
   russia,
   ukraine} => {winner} 0.002444988        1.0 2.655844

   lhs              rhs          support confidence     lift
1 {denmark,
    favourites} => {win}    0.017114914 1.0000000 1.520446
2 {denmark,
    ukraine}     => {win}    0.012224939 0.8333333 1.267038
3 {russia,
    ukraine}     => {win}    0.009779951 0.8000000 1.216357
4 {denmark,
    russia}      => {win}    0.009779951 0.8000000 1.216357
5 {denmark,
    russia,
    ukraine}     => {win}    0.009779951 0.8000000 1.216357
6 {holland,
    netherlands} => {win}    0.004889976 1.0000000 1.520446
7 {denmark,
    ireland}     => {win}    0.004889976 0.6666667 1.013631
8 {lithuania,
    moldova}     => {win}    0.002444988 1.0000000 1.520446
9 {ireland,
    ukraine}     => {winner} 0.002444988 1.0000000 2.655844
10 {ireland,
    russia}      => {winner} 0.002444988 1.0000000 2.655844
11 {ireland,
    russia,
    ukraine}     => {winner} 0.002444988 1.0000000 2.655844
12 {denmark,
    ireland,
    ukraine}     => {winner} 0.002444988 1.0000000 2.655844
13 {denmark,
    ireland,
    russia}      => {winner} 0.002444988 1.0000000 2.655844
14 {denmark,
    ireland,
    russia,
    ukraine}     => {winner} 0.002444988 1.0000000 2.655844

1 {netherlands} => {vote}        0.06060606        0.8 2.200000
2 {russia}      => {love}        0.04545455        1.0 3.300000
3 {denmark}     => {win}         0.04545455        0.5 1.375000
4 {moldova}     => {votes}       0.03030303        1.0 16.500000
5 {irish}       => {votes}       0.01515152        1.0 16.500000
6 {holland}     => {netherlands} 0.01515152        1.0 13.200000
7 {holland}     => {win}         0.01515152        1.0 2.750000
8 {belgium}     => {love}        0.01515152        0.5 1.650000
9 {belgium}     => {win}         0.01515152        0.5 1.375000
10 {holland,
    netherlands} => {win}         0.01515152        1.0 2.750000
11 {denmark,
    ukraine}     => {favourite}   0.01515152        1.0 7.333333

lhs          rhs         support confidence      lift
1 {denmark} => {win}          0.24 0.8571429 1.785714
2 {denmark} => {favourite}    0.16 0.5714286 2.857143
3 {belgium} => {win}          0.08 1.0000000 2.083333
4 {ireland} => {win}          0.08 0.5000000 1.041667
5 {ukraine} => {favourite}    0.04 1.0000000 5.000000
6 {serbia} => {fun}          0.04 1.0000000 25.000000
7 {pretty} => {russia}       0.04 1.0000000 8.333333
8 {pretty} => {denmark}      0.04 1.0000000 3.571429
9 {pretty,
    russia} => {denmark}      0.04 1.0000000 3.571429
10 {denmark,
    pretty} => {russia}       0.04 1.0000000 8.333333
11 {denmark,
    russia} => {pretty}       0.04 1.0000000 25.000000