This chapter provides an overview of the application of co-occurrence network analysis in analysing consumer-generated texts. Co-occurrence networks map how often terms appear together in text and group them into meaningful clusters, revealing latent structures and thematic patterns. The technique is used to identify and visualize relationships between words and themes expressed by consumers in surveys, interviews, online reviews, and social media posts. The chapter also describes the steps for constructing the networks including data sourcing, text preprocessing, and the generation of visual graphs where node size, edge thickness, and spatial proximity represent word frequency and association strength. When applied in consumer research, co-occurrence networks are often combined with sentiment analysis, topic modelling, and demographic segmentation to get insights into consumer preference and behaviour. The main advantages of co-occurrence networks technique include the ability to process large volumes of unstructured data without predefined categories and the visual clarity. The limitations include a lack of sentiment discernment, the dependence on the preprocessing process and the need of experience for interpretation.