By looking at the popularity of the leaders during each of the televised debates is it possible to make a prediction on who would be the eventual winner in the actual general election?
This is a difficult question to answer but if we look at the statistics there are some conclusions that appear to be possible from the extrapolation of the data that was mined.
These graphs show the percentage popularity of each the leaders during the three televised debates as well as the final election result in terms of percentage of overall votes cast.
The most striking thing to note is that if the Twitter data is extrapolated out (linearly) it corresponds very closely to the actual election results. The extrapolated result for Cameron was 37%, the actual election results was 36% (Conservatives’ share of total votes cast). The extrapolated result for Clegg was 25%, the actual election result was 23% (Liberal Democrats share of total votes cast).
Clegg’s declining popularity and Cameron’s correspondingly increasing popularity stands out quite clearly from the number of positive Tweets about each potential leader. The TV popularity effect of Clegg did not translate into actual votes but the trend analysis if extrapolated would have predicted that. The increasing popularity for Cameron was conclusive as history now shows.
This case study shows how the power of using NLP with the I2E software platform can be used to gain quite powerful insights on what is likely to happen based on opinions expressed by people using social media platforms.
Linguamatics’ I2E text mining software was used to find and summarize tweets that have the same meaning, however they are worded. I2E identifies the range of vocabulary used in tweets and uses linguistic analysis to collect and summarize the different ways opinion is expressed.