Opinions expressed about a particular subject are often nu- anced: a person may have both negative and positive opinions about different aspects of the subject of interest, and these aspect-specific opinions can be independent of the overall opinion. Being able to identify, collect, and count these nuanced opinions in a large set of data offers more insight into the strengths and weaknesses of competing products and services than does aggregating overall ratings. We contribute a new condence-based co-training algorithm that can identify product aspects and sentiments expressed about such aspects. It offers better precision than existing methods, and handles previously unseen language with aplomb. The algorithm is demonstrated on a set of opinionated sentences about laptops and restaurants from a SemEval-2014 Task 4 challenge; results are competitive.