Consumers’ brand associations are essential to the development of effective marketing strategies. Understanding consumers’ brand associations enables firms to determine their brand’s positioning and informs new product development and marketing mix design. A rich and abundant source for consumers’ brand associations is user-generated-content (UGC). To process these usually big and unstructured data, researchers turned to neural word embeddings such as the popular word2vec model. While word2vec was shown to create new insights for marketing, it suffers from a major shortcoming: Its inability to consider temporal information. Word2vec does not model dynamic changes in language over time. Yet, UGC commonly spans several years during which changing market conditions and evolving product lifecycles lead to changes in consumers’ brand associations. These changes are captured in the UGC consumers generate. Ignoring the dynamics in brand associations can mislead decision makers about the effectiveness of their marketing mix design.