Organizational networks are often hierarchical by nature as individuals take on roles or functions at various job levels. Prior studies have used either text-level (e.g., sentiment, affect) or structural-level features (e.g., PageRank, various centrality metrics) to identify influential nodes in networks. In this study, we use a combination of these two levels of information to develop a novel ranking method that combines sentiment analysis and PageRank to infer node-level influence in a real-world organizational network. We detect sentiment scores for all actor pairs based on the content of their email-based communication, and calculate their influence index using an enhanced PageRank method. Finally, we group individual nodes into distinct clusters according to their influence index. Compared to established network metrics designed or used to infer formal and informal influence and ground truth data on job levels, our metric achieves the highest accuracy for inferring formal influence (60.7%) and second highest for inferring informal influence (69.0%). Our approach shows that combining text-level and structural-level information is effective for identifying the job level of nodes in an organizational network.