The Design And Implementation Of Distributed Text Analysis And Mining Platform Based On Spring Cloud

Enterprises will produce a lot of unstructured data in their process of production and operation.These data,especially text data,contain a lot of business information.It has become a matter of concern to people that how to make full use of these data and mining the hidden value to provide guidance for decision-making.Semantic analysis of text includes three levels:word,sentence and text.At present,there are some mature theories and analysis tools for each level,such as case grammar,latent semantic analysis,etc.The development of these natural language processing technologies makes it possible to realize text analysis and miningDue to the various sources,flexible and diverse expressions of text content,it has a large amount of ambiguity.It is difficult to obtain ideal results by applying analysis tools simply.Besides,most of the data is closely related to business scenarios,so it requires a large number of adjustments to analysis strategy,which greatly increases the implementation cost of the enterprise.In this case,enterprises need an easy-to-use tool that can make some customized adjustments to meet the needs of business to help them achieve their analysis and mining goals.This thesis shows the whole process of designing and implementing a semantic analysis mining platfonn.This platforn adopts OEC modeling technology of Ontology,Element and Concept trinity to realize business modeling.It divides business and language into two different]evels and uses them as basic resources to express the mining strategy of different business needs,and finally outputs structured labels oriented to the business.In terms of architecture,the platform is based on microservice architecture.A flexible and efficient distributed system is constructed through Spring Cloud components and quickly deployed by Docker,making the entire system more scalable.The text analysis and mining platform implemented in this thesis has good analysis and mining capabilities without custom development.The built-in scenario models can help companies to carry out relevant analysis quickly.Among them,the accuracy of models such as sentiment analysis and named entity recognition can reach more than 90%.The response time of each algorithm component is within 10ms on average,and it can be guaranteed within 300ms in high concurrency scenarios.With the increase and improvement of built-in models and algorithms,the platform is expected to become the best choice for companies to conduct text analysis and mining and implement artificial intelligence strategies.