Sentiment Mining for Real Time Insights on Twitter


syKalev Leetaru of Wired recently wrote, "For its flagship new reality show Opposite Worlds the Syfy channel wanted to let the audience 'remote control' the show via social media. I worked with Syfy to create what ultimately became its real-time 'Twitter Popularity Index.' The Index combines the intensity of conversation around each character, the number of unique discussants, and the emotion of that discussion using a new sentiment engine powered by over 1.6 million words, phrases and common misspellings and colloquial expressions. Using our Index, Opposite Worlds records across the board in Twitter engagement for a cable television series."


Leetaru continues, "Sentiment mining is a hot emerging field, yet the underlying technology has changed little from the first computerized sentiment mining system created in 1961, the General Inquirer. It still treats emotion measurement as merely a technical problem. This has yielded a stream of pioneering technical achievements that have focused on algorithms rather than the actual outcome of how to better measure tone online. To build the first sentiment engine that could actually understand real-time tweets, we had to start from scratch, asking the question: how can big data combine with human insight to change the way we interact with our world? In the process, we identified 16 limitations to current sentiment mining approaches."


Read more here.