Adoption of Software Product Line Engineering (SPLE) to support systematic reuse of software-related artifacts within product families is challenging, time-consuming and error-prone. Analyzing the variability of existing artifacts needs to reflect different perspectives and preferences of stakeholders in order to facilitate decisions in SPLE adoption. Considering that requirements drive many development methods and activities, we introduce an approach to analyze variability of behaviors as presented in functional requirements. The approach, called semantic and ontological variability analysis (SOVA), uses ontological and semantic considerations to automatically analyze differences between initial states (preconditions), external events (triggers) that act on the system, and final states (post-conditions) of behaviors. The approach generates feature diagrams typically used in SPLE to model variability. Those diagrams are organized according to perspective profiles, reflecting the needs and preferences of the potential stakeholders for given tasks. We conducted an empirical study to examine the usefulness of the approach by comparing it to an existing tool which is mainly based on a latent semantic analysis measurement. SOVA appears to create outputs that are more comprehensible in significantly shorter times. These results demonstrate SOVA's potential to allow for flexible, behavior-oriented variability analysis.