Ranking of Urban Brand Influence Based on Social Media Comment Mining

Abstract

Social media reviews play an increasingly important role in ranking the influence of urban brands. In this study, the public social media review database is mined, and a regression model of urban influence is established. Firstly, this study introduces text mining and data collection. Comment data are collected from static websites and dynamic websites, and ICTCLAS word segmentation tool is used to preprocess the comment data. The algorithm of urban influence level is established, and finally the regression model of urban brand influence is established. A database of 10000 city-related reviews was used in the experiment. The gender thesaurus is established to further improve the accuracy of the experimental results. The feasibility of the model is verified from two aspects: expectation calculation and standardization. This paper analyzes the composition of citizens who comment on social media and the proportion of comment content and puts forward some suggestions to enhance the influence of the city. Finally, it summarizes the popular comments in different months in December and obtains the force points to enhance the influence of the city in different time periods from the structure.

The Basic Principles of Marxism with the Internet as a Carrier

“…is study, based on the understanding and recognition of the characteristics of big data, also requires technology as a means to bring the value of big data into play with the various algorithms that are popular and common today, and most of these algorithms are already relatively well developed and mature. e use of computer algorithmic techniques to mine data on philosophical ideas in the web and the ideologies developed from them is the only way to demonstrate the true value of big data in terms of the field of Marxist studies [21].…”