Polls have been used for decades as a tool to gauge public opinion on specified topics. Originally used in US Presidential elections, they are now used to gather point-of-time information on politicians, political issues, brand names, products, even prospective storylines in movies. Robust polls typically specify a priori an acceptable level of sampling error e.g. +/- 3% in order to calculate the size of the random sample needed. While these error rates are acceptable for most cases, having to manually poll a random sample of up to 1,000 people means opinion polls are expensive to carry out. In order to identify secular trends, polling must be repeated at frequent intervals, which makes monitoring for trends even more expensive. Surveys can prove more cost-effective, but still require resources to recruit participants and collate results, with the risk of latency from time of issuing a survey to aggregation of results. For this reason we look to online social media as a potential low-cost, continuous and scalable alternative source for opinion mining that can be readily replicated.