Privacy code review is a critical process that enables developers and legal experts to ensure compliance with data protection regulations. However, the task is challenging due to resource constraints. To address this, we introduce the concept of privacy-relevant methods - specific methods in code that are directly involved in the processing of personal data. We then present an automated approach to assist in code review by identifying and categorizing these privacy-relevant methods in source code. Using static analysis, we identify a set of methods based on their occurrences in 50 commonly used libraries. We then rank these methods according to their frequency of invocation with actual personal data in the top 30 GitHub applications. The highest-ranked methods are the ones we designate as privacy-relevant in practice. For our evaluation, we examined 100 open-source applications and found that our approach identifies fewer than 5% of the methods as privacy-relevant for personal data processing. This reduces the time required for code reviews. Case studies on Signal Desktop and Cal.com further validate the effectiveness of our approach in aiding code reviewers to produce enhanced reports that facilitate compliance with privacy regulations.