Abstract: Techniques are disclosed for filtering and analyzing social network content so that consumer sentiment can be gauged more accurately and efficiently. In certain embodiments social network content can be filtered so that individual content items can be identified as comprising neutral, sentiment bearing, spam or foreign language content. Such filtering can be performed by marking certain features that are indicative of a particular type of content, and then using machine learning systems to classify individual content items based on the marked features. A portion of the filtered content, such as only the items containing sentiment bearing content, can then be subjected to sentiment analysis. The results of this sentiment analysis can be presented to a social network campaign manager via a sentiment browser interface, optionally with the underlying filtered content. This allows the campaign manager to easily view the results of the sentiment analysis with the filtered social network content.