  This is a report, where preliminary work regarding the topic of voting
intention inference from Social Media - such as Twitter - is presented. Our
case study is the UK 2010 General Election and we are focusing on predicting
the percentages of voting intention polls (conducted by YouGov) for the three
major political parties - Conservatives, Labours and Liberal Democrats - during
a 5-month period before the election date (May 6, 2010). We form three
methodologies for extracting positive or negative sentiment from tweets, which
build on each other, and then propose two supervised models for turning
sentiment into voting intention percentages. Interestingly, when the content of
tweets is enriched by attaching synonymous words, a significant improvement on
inference performance is achieved reaching a mean absolute error of 4.34% +/-
2.13%; in that case, the predictions are also shown to be statistically
significant. The presented methods should be considered as work-in-progress;
limitations and suggestions for future work appear in the final section of this
script.
