Ambiguity in natural language-based requirements is a well-known issue, often addressed as a singular problem despite its complexity. Studies reveal that ambiguity in user stories can manifest differently depending on linguistic levels. This study introduces the ambiguity analysis framework (AmbiTRUS) to address these diverse manifestations by composing quality criteria for 13 types of ambiguity problems, classified across four linguistic levels and linked to four types of requirements quality problems. The proposed quality criteria were selected and adapted from three established user story quality frameworks: the QUS framework, the Agile Requirements Verification framework, and the INVEST framework. To assess the potential effectiveness of AmbiTRUS, we conducted a controlled laboratory experiment with advanced MSc students representing novice practitioners of the intended users of the framework. While the experiment did not demonstrate clear effectiveness, users found the framework useful despite its complexity. Insights from the experiment allowed for redefining the framework’s quality criteria. The main lesson learned from the experiment is the need for tool support in applying AmbiTRUS, particularly using NLP techniques to verify the quality criteria. The development of such an NLP-based tool and the evaluation of AmbiTRUS through a usability study of the tool are the next steps in our research.