Elections’ Epilogue: What Have We Learned?

First the good news: a MAD of 1.41 Gets the Bronze Medal of All Polls!

The results from the Flemish Parliament elections with all votes counted are:

Party	Results (source: Het Nieuwsblad)	SAM’s forecast
Christian democrats (CD&V)	20,48 %	18,70 %
Green (Groen)	8,7 %	8,75 %
Flemish nationalists (N-VA)	31,88 %	30,32 %
Liberal democrats (open VLD)	14,15 %	13,70 %
Social democrats (SP-A)	13,99 %	13,27 %
Nationalist & Anti-Islam party (VB)	5,92%	9,80%

Table1. Results Flemish Parliament compared to our forecast

And below is the comparative table of all polls compared to this result and the Mean Absolute Deviation (MAD) which expresses the level of variability in the forecasts. A MAD of zero value means you did a perfect prediction. In this case,with the highest score of almost 32 % and the lowest of almost six % in only six observations  anything under 1.5 is quite alright.

Table 2. Comparison of all opinion polls for the Flemish Parliament and our prediction based on Twitter analytics by SAM.

Compared to 16 other opinion polls, published by various national media our little SAM (Social Analytics and Monitoring) did quite alright on the budget of a shoestring: in only 5.7 man-days we came up with a result, competing with mega concerns in market research.

The Mean Absolute Deviation covers up one serious flaw in our forecast: the giant shift from voters from VB (The nationalist Anti Islam party) to N-VA (the Flemish nationalist party). This led to an underestimation of the N-VA result and an overestimation  of the VB result. Although the model estimated the correct direction of the shift, it underestimated the proportion of it.

If we would have used more data, we might have caught that shift and ended even higher!

Conclusion

Social Media Analytics is a step further than social media reporting as most tools nowadays do. With our little SAM, built on the Data2Action platform, we have sufficiently proven that forecasting on the basis of correct judgment of sentiment on even only one source like Twitter can produce relevant results in marketing, sales, operations and finance. Because, compared to politics, these disciplines deliver far more predictable data as they can combine external sources like social media with customer, production, logistics and financial data. And the social media actors and opinion leaders certainly produce less bias in these areas than is the -case in political statements. All this can be done on a continuous basis supporting day-to-day management in communication, supply chain, sales, etc...

If you want to know more about Data2Action, the platform that made this possible, drop me a line: contact@linguafrancaconsulting.eu 

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The Last Mile in the Belgian Elections (II)

Getting Started

I promised to report on my activities in social analytics. For this report, I will try to wear the shoes of a novice user and report, without any withholdings about this emerging discipline. I explicitly use the word “emerging” as it has all the likes of it: technology enthusiasts will have no problem overlooking the quirks preventing an easy end to end “next-next-next” solution. Because there is no user friendly wizard that can guide you from selecting the sources, setting up the target, creating the filters and optimising the analytics for cost, sample size, relevance and validity checks, I will have to go through the entire process in an iterative and sometimes trial-and-error way.

This is how massive amounts of data enter the file system

Over the weekend and today I have been mostly busy just doing that. Tweet intakes ranged from taking in 8.500 Belgian tweets in 15 seconds and doing the filtering locally on our in memory database to pushing all filters to the source system and getting 115 tweets in an hour. But finally, we got to an optimum query result and the Belgian model can be trained. The first training we will set up is detecting sarcasm and irony. With the proper developed and tested algorithms we hope for a 70% accuracy in finding tweets that express exactly the opposite sentiment of what the words say. Tweets like “well done, a**hole” are easy to detect but it’s the one without the description of the important part of the human digestive system that’s a little harder.

The cleaned output is ready for the presentation layer

Conclusion of this weekend and today: don’t start social analytics like any data mining or statistical project. Because taming the social media data is an order of magnitude harder than crunching the numbers  in stats.

Let’s all cross our fingers and hope we can come up with some relevant results tomorrow.

The Last Mile in the Belgian Elections

Sentiment Analysis, a Predictor of the Outcome?

Data2Action is an agile data mining platform consisting of efficiently integrated components for rapid application development. One deliverable of Data2Action is SAM, for Social Analytics and Monitoring.

In the coming days, I will publish the daily results from sentiment analysis on Twitter with regards to the programmes, the major candidates and interest groups.

Stay tuned for the first report on Monday 19th May

Questions like:

• Which media produce the most negative or positive tweets about which party, which major candidate?
• Who are the major influencers on Twitter?
• What are the tweets with the highest impact?

The major networks will stimulate lots of tweets this weekend so we will present the analysis next Monday.