Spatialization Method of Atmospheric Quality Public Opinion Based on Natural Language Processing

With the increasing environmental awareness of the country and the people and the implementation of strong environmental protection measures, China’s atmospheric quality was improved obviously, but local atmospheric pollution events still occurred frequently. In order to make up for the missing detection of local atmospheric pollution events caused by sparse fixed State-controlled monitoring stations, the paper proposed a spatialization method for atmospheric quality public opinion information based on natural language processing. Using Chinese word segmentation, part of speech tagging and other methods, the paper extracted addresses from public atmospheric pollution complaints data. Through an effective combination of those addresses, the paper realized address matching of those complaint points, and spatialized those key complaint areas in Shandong Province in the form of heat map. Through comparison and analyzing with the atmospheric quality monitoring data of national control stations, it showed that the key areas of public complaints were highly consistent with the key pollution areas which were monitored by national control stations. The research result showed that the public could perceive the atmospheric quality directly, and reflect the local atmospheric pollution at a smaller space-time scale effectively, which was a robust supplementation to the monitoring data of the national control station.