【内容摘要】When quantifying information from unstructured textual data, the traditional bag-of-words approach only captures semantic features of single words or phrases. The context, the sequence of words, and the relationship between words are ignored. This paper introduces a novel approach to incorporate complex syntactical features in the textual analysis using two deep neural network (NN)-based methods. We construct a new measure of sentiment that is specific to performance discussions and is adjusted for complex contextual negations. We find that this performance-specific sentiment explains cross-sectional returns and future operating performance better than the umbrella sentiment proxies used in the literature.