基于人工智能背景下无线网络爬虫技术的高校舆情分析

Wenning Wu, Zheng-hong Deng
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引用次数: 0

摘要

由于wi - fi技术的快速发展,支持wi - fi的信息终端变得更加快速和强大。由此,人工智能(AI)领域诞生。人工智能(AI)已经在广泛的社会环境中得到应用。它对教育领域产生了重大影响。利用大数据支持每个意见主体的多阶段观点,有助于识别每个方面的独特特征,提高社会网络治理的适用性。随着高校舆情日益成为民意表达的重要载体,本文旨在探索基于网络爬虫和CNN(卷积神经网络)模型的舆情概念。利用网络爬虫方法收集高校学生给出的数据,并在不同的维度上提及他们。该CNN具有强大的数据分析能力;该模型使用CNN对民意进行分析。采用过采样方法对数据进行预处理,使分类效果最大化。通过对描述的关联,综合利用用户影响力、评论立场、话题、评论时间等图像信息,对各种方案提出引导现象,有助于增强网络社会治理的有效性和针对性。整体实验是在python中进行的,其中建议的方法是预测学生对网络爬虫技术的积极和消极意见,与其他现有方法相比,错误率低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Public Opinion in Colleges and Universities Based on Wireless Web Crawler Technology in the Context of Artificial Intelligence
Wi-Fi-enabled information terminals have become enormously faster and more powerful because of this technology’s rapid advancement. As a result of this, the field of artificial intelligence (AI) was born. Artificial intelligence (AI) has been used in a wide range of societal contexts. It has had a significant impact on the realm of education. Using big data to support multistage views of every subject of opinion helps to recognize the unique characteristics of each aspect and improves social network governance’s suitability. As public opinion in colleges and universities becomes an increasingly important vehicle for expressing public opinion, this paper aims to explore the concepts of public opinion based on the web crawler and CNN (Convolutional Neural Network) model. Web crawler methodology is utilised to gather the data given by students of college and universities and mention them in different dimensions. This CNN has robust data analysis capability; this proposed model uses the CNN to analyse the public opinion. Preprocessing of data is done using the oversampling method to maximize the effect of classification. Through the association of descriptions, comprehensive utilization of image information like user influence, stances of comments, topics, time of comments, etc., to suggest guidance phenomenon for various schemes, helps to enhance the effectiveness and targeted social governance of networks. The overall experimentation was carried out in python here in which the suggested methodology was predicting the positive and negative opinion of the students over the web crawler technology with a low rate of error when compared to other existing methodology.
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