Design And Implementation Of Social Big Data Information Platform

The rapid development of the Internet has made domestic and foreign media transition from traditional media to the new media era.Various self-media platforms have emerged in an endless stream,and a large number of self-media people have emerged.Self-media people's creative content attracts a group of fans to subscribe to their operating accounts.Through the interaction of fans with their creations,the analysis of data can best reflect the operation.Fan attitudes and comments are the most intuitive feedback to the content of the creation,which is helpful for the media people to understand their fans' opinions and views and directly hit the fans' inner thoughts.On the other hand,the public opinion can be understood through the attitude formed by the majority of fans' comments on the views of hot events.The integrated analysis of these scattered and fragmented views has an important role in understanding the brand effect of self-media and for making favorable decisions for the government and consulting companies to understand popular events and track public opinion.This article completes the platform's needs analysis by deeply digging user needs,analyzing product characteristics,and identifying product positioning.The platform is divided into three major functional modules through demand analysis: social big data collection module,self-media comment sentiment classification module,and information platform Integrated business module.Through the overall design from top to bottom and the structure of the platform database table,the functional structure under each module is refined,and the detailed design and implementation of each module are carried out and the platform testing task is completed.(1)Social big data collection module: This module provides a data source for the platform.It mainly uses distributed crawler architecture to download web pages,extract information,and store and store data.Mainly designed to complete functions such as web crawler download,URL deduplication strategy,data crawling architecture design and so on.(2)Self-media comment sentiment classification module: using natural language processing and machine learning classification algorithms to train sentiment classification models on review data,divided into positive,negative,and middle categories of sentiment polarity,transforming unstructured text data into Analyze structured data to dig deeper into the value of massive unstructured text data.(3)Information platform integrated business module: processing platform business logic,analyzing social big data collected,evaluating self-media accounts from multiple dimensions,platform will perform diverse front-end display,with good-looking design interface,good User experience.The various modules of the system cooperate organically.After repeated testing,the social big data information platform can run stably.The platform can meet the collection,analysis and visual display of self-media information.By using this platform,you can quickly understand the various information of many self-media people with influential self-media,and the direction of fan public opinion.In order to help brands,merchants and self-media people to better cope with the changing network environment,find public opinion response measures faster.