The Net Promoter Score (NPS) is a widely-used metric for measuring customer loyalty and is an essential element in customer service analysis. To understand and analyze NPS effectively, customer-generated content, such as customer reviews, is commonly employed. Nevertheless, the information provided in customer-generated content may sometimes be inadequate. In this paper, we aim to enhance NPS understanding by incorporating a valuable employee-generated content known as employee notes, which provide insights into the internal operations of the enterprise's customer service. Considering the different standpoints of the two pieces of textual information, the semantic consistency is defined to calculate the semantic relationship between them, which helps better understand and analyze NPS. To effectively capture the crucial semantic consistency relationship and enhance the understanding of NPS, we propose a hierarchical deep learning approach, called RNSC (Review, Note and Semantic Consistency), which employs a bidirectional LSTM (BiLSTM) and a semantic consistency recognition module to encode and identify the local relationship between customer reviews and employee notes. Based on the trained model, the attention mechanism can detect and output the keywords that significantly contribute to the NPS through semantic consistency, showing its potential for supporting diagnostic analysis and further service optimization. Extensive data experiments, such as comparison with baseline methods, ablation experiments, and sparse data experiments, verify the superiority of the proposed RNSC approach.