This chapter demonstrates the potential of NLA and LLMs to extract valuable insights from text data. By analyzing qualitative data related to climate change, a KSI that quantifies the prevailing anxiety score is developed. The aim is to showcase the potential of LLMs to convert text into soft data and soft data into actionable insights, offering decision-makers a valuable tool for understanding and addressing concerns across diverse domains. This chapter presents an illustration (which includes implicit poetic license on data handling) then exemplifying suitable approaches to handle and interpret soft data in a NLA context. The case study utilizes a dataset comprising by 1,691 climate-related headlines, scraped from two different search engines. The concept of interpretants, derived from semiotics, are used to classify and interpret the headlines into immediate, dynamical, and final interpretants, helping reveal how different climate-related narratives are perceived, providing a qualitative dimension to the analysis. Visualization is used to aid both creative and analytical thinking.