In today’s hyper-connected world, rich social data feeds can be obtained from various sources, including the data exhaust of many commonly used systems. In this paper, we analyze the social pulse, obtained from viewer activity in an IPTV network-we attempt to validate a framework for determining public opinion and public interest through implicit feedback of IPTV viewers. First, we address the hypothesis that implicit viewer feedback in the form of channel change events, paired with the content metadata, can be used to model viewers’ opinion and interest. For this, we design a controlled experiment to collect explicit feedback by rating a set of general-interest news clips. In addition to collecting demographic information, we also survey viewers’ opinion, interest, and the probability of channel change during each clip. Furthermore, we extract weighted feature vectors from the closed captions of the video; this data, combined with the reported probability of channel change, is used to build a model that classifies opinion in five categories based on the probability of channel change and content. Next, we build a simplified model that classifies opinion in five categories based on the interest, which shows a linear relationship, but further consideration of content, in this case, provides better accuracy and possibility to analyze anomalous cases. Finally, we discuss and analyze the applications of such models in large systems and the necessary modifications to scale the system and to ensure the adequate performance on massive IPTV event data streams.