A user demand acquisition method for cloud services based on user sentiment analysis and long- and short-term preferences

Abstract

The use of new technologies to extract users’ needs and provide them with personalized services has become a development trend. Rely on cloud platform data, to recommend personalized services to users more accurately, the emotional preferences of users are combined with their long- and short-term interests. Moreover, a personalized cloud platform service recommendation model, called the multi-directional aggregate graph convolutional network (MGCN) is proposed. First, the importance of online text analysis in the field of cloud service platforms is summarized, as is the lack of research on the acquisition of user needs based on online text. Second, the relevant theoretical knowledge and models of online text acquisition are explored. Third, a personalized cloud platform service recommendation model is proposed based on the optimized GCN models to analyze the emotional preferences and long- and short-term interests of users. Finally, the feasibility of the improved methodology are verified using the relevant Q&A data of the top 100 most active users on three professional cloud service platforms in China. The findings provide new concepts for the front-end construction of cloud service platforms.