The assessment and analysis of public opinion and people’s interest in various topics have been highly important for decades. Every major social, economic or political decision process relies on tapping the pulse of the public opinion through time, and tries to adjust based on the feedback. In this work the focus is on channel change events (CCE) generated by the viewers. CCE data can be represented by a time series vector; it hides a wealth of user behavior information, as each channel change event is motivated by a combination of viewers interests and content context. The key challenge addressed in the paper is to demonstrate how the users inter actions with the IPTV service can be efficiently used to gauge the public interest on a specific topic at a large scale. To address the challenges of using an implicit feedback event stream of an IPTV system to infer public interest and opinion on a large scale, proposes a framework that leverages a variety of research domain.

Individuals mining input curation assessing IPTV channel change responses