一句话：提出 POME，用自然语言解析与模式匹配对 Stack Overflow 中关于 API 的句子进行方面分类和极性判定，精度优于 Opiner。
Q&A 网站（如 Stack Overflow）包含大量非正式文档，开发者可从中获得 API 用法示例及其优缺点观点，但人工搜索和判断成本高。论文提出 POME，一种基于模式的观点挖掘方法，利用自然语言解析和模式匹配，将涉及 API 的 Stack Overflow 句子按性能、可用性等七个方面分类，并判定正负极性。模式来自对 30 个 API 相关的 4,346 个句子的手工分析。评估包括：与以这些模式及 n-gram 为特征的机器学习方法比较；与情感分析工具比较极性判定；并与当前最优的 Stack Overflow 观点挖掘方法 Opiner 进行 24 名人类评估者参与的研究。结果表明，POME 在观点方面识别和极性评估上均比 Opiner 具有更高精度。
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 APIs, as well as opinions about pros and cons of such APIs. Automatically identifying and classifying such opinions can alleviate developers' burden in performing manual searches, and can be used to recommend APIs 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 APIs according to seven aspects (e.g., performance, usability), and to determine their polarity (positive vs negative). The patterns have been inferred by manually analyzing 4,346 sentences from Stack Overflow linked to a total of 30 APIs. 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.