Analysing TV Audience Engagement via Twitter: Incremental Segment-Level Opinion Mining of Second Screen Tweets
To attract and retain a new demographic of viewers, television producers have aimed to engage audiences through the “second screen” via social media. This paper concerns the use of Twitter during live television broadcasts of a panel show, the Australian Broadcasting Corporation’s political and current affairs show Q&A, where the TV audience can post tweets, some of which appear in a tickertape on the TV screen and are broadcast to all viewers. We present a method for aggregating audience opinions expressed via Twitter that could be used for live feedback after each segment of the show. We investigate segment classification models in the incremental setting, and use a combination of domain-specific and general training data for sentiment analysis. The aggregated analysis can be used to determine polarizing and volatile panellists, controversial topics and bias in the selection of tweets for on-screen display.
KeywordsSocial TV Opinion mining Machine learning Social media
Thanks to Data to Decisions Cooperative Research Centre for supporting this research and supplying full access to the Twitter data for this paper.
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