基于神经网络词嵌入的大数据关注热点和词嵌入概貌比较研究OACHSSCDCSTPCD

[Purpose/Significance]Big data have significant impacts on social and economic development.This study compares focus of attention and word embedding general picture of big data between academic platforms and social Q&A platforms in China,with the aim of promoting big data research and practice in China.[Method/Process]Word2vec is an emerging neural network word embedding algorithm with low computational cost and high accuracy,and can effectively measure word similarity at both semantic and syntactic levels.Data were first collected from CNKI(China Knowledge Infra-structure)and Zhihu platforms separately to build corpora of academic and social Q&A platforms.Then,the Word2vec models were trained based on these two corpora respectively.Based on the analysis of the most similar words,comparisons of focus of attention of big data between academic and social Q&A platforms were conducted.Finally,dimension reduction and visualization algorithm were employed to conduct comparison of word embedding general picture between two platforms.[Result/Conclusion]The results illustrate the differences of big data between academic and social Q&A platforms.This study innovatively employs the Word2vec neural network word embedding algorithm to conduct a comparative analysis of big data between academic platforms and social Q&A platforms in China,providing a new perspective for big data research.