This paper presents a novel approach to analyze the sentiment of the product comments from sentence to document level and apply to the customers sentiment analysis on UAV-aided product comments for hotel management. In order to realize the effiffifficient sentiment analysis, a cascaded sentence-to-document sentiment classifification method is investigated. Initially, a supervised machine learning method is applied to explore the sentiment polarity of the sentence (SPS). Afterward, the contribution of the sentence to document (CSD) is calculated by using various statistical algorithms. Lastly, the sentiment polarity of the document (SPD) is determined by the SPS as well as its contribution. Comparative experiments have been established on the basis of hotel online comments, and the outcomes indicate that the proposed method not only raises the effiffifficiency in attaining a more accurate result but also assists immensely in regards to the B5G wireless communication supported by the UAV. The fifindings provide a new perspective that sentence position and its sentiment similarity with document (sentiment condition) dramatically disclose the relationship between sentence and document.