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

On voting intentions inference from Twitter content: a case study on UK 2010 General Election