Informal documentation contained in resources such as Q&A websites (e.g., Stack Overflow) is a precious resource for developers, who can find there examples on how to use certain libraries, as well as opinions about pros and cons of such libraries. Automatically identifying and classifying such opinions can alleviate developers’ burden in performing manual searches, and can be used to recommend libraries that are good from some points of view (e.g., performance), or highlight those less ideal from other perspectives (e.g., compatibility). We propose POME (Pattern-based Opinion MinEr), an approach that leverages natural language parsing and pattern-matching to classify Stack Overflow sentences referring to libraries according to seven aspects (e.g., performance, usability), and to determine their polarity (positive vs negative). The patterns have been inferred by manually analyzing 4,363 sentences from Stack Overflow linked to a total of 30 libraries. We evaluated POME by (i) comparing the pattern-matching approach with machine learners leveraging the patterns themselves as well as n-grams extracted from Stack Overflow posts; (ii) assessing the ability of POME to detect the polarity of sentences, as compared to sentiment-analysis tools; (iii) comparing POME with the state-of-the-art Stack Overflow opinion mining approach, Opiner, through a study involving 24 human evaluators. Our study shows that POME exhibits a higher precision than a state-of-the-art technique (Opiner), in terms of both opinion aspect identification and polarity assessment.