基于Python的农村土地流转新闻文本采集与分析

doi:10.13998/j.cnki.issn1002-1248.2019.10.17-0917

Abstract

Abstract: [Purpose / Significance] In order to quickly obtain a massive amount of data of land transfer on the Internet, with the help of the current popular method of data collection and analysis-web crawler technology, the new hot spots of rural land transfer in China are excavated and sorted out from the perspective of news and information. [Method / Process] Taking People's Daily as the target web page, we search keywords on the web page and use Python to write and design programs to crawl the news documents of farmland circulation on the web page. and We then use the obtained more than 2 500 pieces of news as data to analyze the status, content and problems of agricultural land transfer by using text analysis methods and drawing Python word cloud diagrams. [Results / Conclusions] Crawling the web news information, through statistical analysis of the collected news content, after hot spot mining, it is found that China is attaching more importance to the transfer of agricultural land and increased publicity in this regard, The construction of agricultural land transfer and precision poverty alleviation, and rural tourism and cooperatives are closely connected with one another. At last we analyze the existing problems in above-mentioned three aspects and make recommendations.

Key words: web crawler, text collection, text analysis, land transfer

CLC Number:

G354.2

LIU Xindong, WU Jinwen, LIU Dingyan, ZHOU QiaoYi. Collection and Analysis of Rural Land Circulation News Text Based on Python[J].Journal of Library and Information Science in Agriculture, 2020, 32(5): 76-81.

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