Power Customer Satisfaction Based on Power Big Data and NLP

With the progress of the times and the development of the economy, competition between enterprises is no longer limited to quality and price competition. Service has gradually become the core of enterprise competition, and customer service quality has become a major research hotspot. In the new situation, due to the limited resources of power customers, power supply companies must consider how to successfully prevent customer churn and improve customer stickiness in a fiercely competitive environment. In order for customers to choose themselves as power suppliers, the quality of power products and services provided by power supply companies must ensure customer satisfaction. Based on this, this paper discussed the power customer satisfaction analysis method based on power Big data and NLP, first introduced the power Big data and natural language processing, and then discussed how to build the power customer satisfaction analysis framework design. Finally, the experiment contrasted and analyzed the satisfaction analysis system constructed by the fish swarm algorithm optimized BP neural network, structural equation model, fuzzy comprehensive evaluation method, natural language processing (NLP) and power Big data, Naive Bayesian classification algorithm, and stacked denoising auto encoder (SDAE) and classification and region tree (CART) joint intelligent algorithm. The experiment proved the superiority of the power customer satisfaction analysis method based on the technology in this paper (the similarity of the power customer satisfaction analysis method based on Big data (BD) and NLP was 11, 10, 8, 21, and 19% higher than that of the fish swarm optimization BP neural network, structural equation model, fuzzy comprehensive evaluation method, Naive Bayesian classification algorithm, and SDAE and CART joint intelligent algorithm).