After the surprisingly accurate result of the Brexit referendum tweet tracker, we decided to roll it out to the US Presidential Election.
This posed a bit more of a challenge. Unlike Brexit, where we had some fairly obvious hashtags used by either party, there are quite a large number of neutral tweets we were collecting under the hashtag ‘Election2016’, and a lot of tweets attacking candidates rather than necessarily showing support for one.
To address this, we had to build a custom corpus, manually classifying a few hundred tweets before using lessons from that to automate it. Once this corpus was built up, we are capable of classifying tweets as they are collected.
The base tool was configured within three days to analyse the data and present the findings live. It scaled to over 150 tweets/second.
How did we do it?
Similar to the run up to the EU Referendum, we wanted to analyse mass public opinion on the two Presidential Candidates. We took the concept from idea to live in 10 working days, with some modifications to the system. The tool is a reusable solution that organisations can customize to their needs: it is low cost, easy to use and quick to deliver.
The results are simply a reflection of the tweets we have collected. This election has been and this is consistent with the results we have seen. The Twitter data stream is potentially very biased and the work here is not meant to be a forecast of the election result.