zaterdag 24 mei 2014

The Last Mile in the Belgian Elections (VII)

The Flemish Parliament’s Predictions

Scope management is important if you are on a tight budget and your sole objective is to prove that social media analytics is a journey into the future. That is why we concentrated on Flanders, the northern part of Belgium. (Yet, the outcome of the elections for the Flemish parliament will determine the events on Belgian level: if the N-VA wins significantly, they can impose some of their radical methods to get Belgium out of the economic slump which is not very appreciated in the French speaking south.)  In commercial terms, this last week of analytics would have cost the client 5.7 man-days of work. Compare this to the cost of an opinion poll and there is a valid add on available for opinion polls as the Twitter analytics can be done a continuous basis. A poll is a photograph of the situation while social media analytics show the movie.

 A poll is a photograph of the situation while social media analytics show the movie.

From Share-of-Voice to Predictions


It’s been a busy week. Interpreting tweets is not a simple task as we illustrated in the previous blog posts. And today, the challenge gets even bigger. To predict the election outcome in the northern, Dutch speaking part of Belgium on the basis of sentiment analysis related to topics is like base-jumping knowing that not one, but six guys have packed your parachute. These six guys are totally biased. Here are their names, in alphabetical order, in case you might think I am biased:


Dutch name
Name used in this blog post
CD&V (Christen Democratisch en Vlaams)
Christian democrats
Groen
Green (the ecologist party)
N-VA (Nieuw-Vlaamse Alliantie)
Flemish nationalists
O-VLD (Open Vlaamse   Liberalen en Democraten)
Liberal democrats
SP-A (Socialistische Partij Anders)
Social democrats
VB (Vlaams Belang)
Nationalist & Anti-Islam party
Table 1 Translation of the original Dutch party names

From the opinion polls, the consensus is that the Flemish nationalists can obtain a result over 30 % but the latest poll showed a downward trend breach, the Nationalist Anti-Islam party will lose further and become smaller than the Green party. In our analysis we didn’t include the extreme left wing party PVDA for the simple reason that they were almost non-existent on Twitter and the confusion with the Dutch social democrats created a tedious filtering job which is fine if you get a budget for this. But since this was not the case, we skipped them as well as any other exotic outsider. Together with the blanc and invalid votes they may account for an important percentage which will show itself at the end of math exercises. But the objective of this blog post is to examine the possibilities of approximating the market shares with the share of voice on Twitter, detect the mechanics of possible anomalies and report on the user experience as we explained at the outset of this Last Mile series of posts.

If we take the rough data of the share-of-voice on over 43.000 tweets we see some remarkable deviations from the consensus.
Party
Share of voice on Twitter
Christian democrats
21,3 %
Green (the ecologist party)
8,8 %
Flemish nationalists
27,9 %
Liberal democrats
13,6 %
Social democrats
12,8 %
Nationalist & Anti-Islam party
11,3 %
Void, blanc, mini parties
4,3 %

Table 2. Percentage share of voice on Twitter per Flemish party

It is common practice nowadays to combine the results of multiple models instead of using just one. Not only in statistics is this better, Nobel prize winner Kahneman has shown this clearly in his work. In this case we combine this model with other independent models to come to a final one.
In this case we use the opinion polls to derive the covariance matrix.
Table 3. The covariance matrix with the shifts in market shares 
This allows us to see things such as, if one party’s share grows, at which party’s expense is it? In the case of the Flemish nationalists it does so at the cost of the Liberal democrats and the Nationalist and Anti-Islam party but it wins less followers from the Christian and the social democrats. The behaviour of Green and the Nationalist and Anti-Islam party during the opinion polls was very volatile, which explains for a part the spurious correlations with other parties.


Graph 1 Overview of all opinion poll results: the evolution of the market shares in different opinion polls over time.

Comparing the different opinion polls, from different research organisations, on different samples is simply not possible. But if you combine all numbers in a mathematical model you can smooth a large part of these differences and create a central tendency.
To combine the different models, we use a derivation of the Black-Litterman model used in finance. We are violating some assumptions such as general market equilibrium which we replace by a total different concept as opinion polls. However the elegance of this approach allows us to take into account opinions, confidence in this opinion and complex interdepencies between the parties. The mathematical gain is worth the sacrifice of the theoretical underpinning.
This is based on a variant of the Black-Litterman model  μ=Π+τΣt(Ω+τPΣPt)(pPΠ)


And the Final Results Are…


Party
Central Tendency of all opinion polls
Data2Action’s Prediction
18 %
18,7 %
Green (the ecologist party)
8,7 %
8,8 %
31 %
30,3 %
14 %
13,7 %
13,3 %
13,3 %
9,4 %
9,8 %
Other (blanc, mini parties,…)
5,6 %
5,4 %
Total
100 %
100 %

Table 4. Prediction of the results of the votes for the Flemish Parliament 

Now let’s cross our fingers and hope we produced some relevant results.

In the Epilogue, next week, we will evaluate the entire process. Stay tuned!






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