Research and Application of Cloud Computing Platform Monitoring System Based on Virtual Cluster

This paper proposes a sentiment recognition algorithm for digital market text data based on natural language processing (NLP) technology. The algorithm uses the global vector (GloVe) model to represent words, which can better capture the semantic relationship between words. In the algorithm design, the paper first preprocess the text data, including noise removal, word segmentation and word frequency analysis. Then, the word vector generated by GloVe is used as input to perform sentiment classification through a deep learning model. The simulation results of the model show that the accuracy of the algorithm reaches 87.6% and the F1 value is 85.3% in the user comments and product evaluation data related to the digital market. Compared with traditional sentiment analysis methods, it has higher accuracy and stability. In addition, this paper also conducts detailed data analysis and discussion on the model performance under different parameter settings, further verifying the effectiveness and adaptability of the algorithm. Future research will further optimize the model structure to improve the recognition ability of complex emotional expressions.