Analysis of Residents'Low-Carbon Travel Intentions Based on Social Media Text Mining
Urban transportation is an important area for carbon reduction,and the proportion of carbon emissions from urban residents'travel has reached 20%.Low carbon travel is of great significance for mitigating global climate change.Understanding residents'behavioral intentions towards low-carbon travel helps in promoting such practices,and social media platforms provide a wealth of valuable information.This paper analyzes residents'behavioral intentions and focus themes regarding low-carbon travel using Sina Weibo posts data,employing text mining methods of BERT-BiLSTM model and the LDA topic model.The results indicate that residents generally have a positive intention towards low-carbon travel,subways and buses are the most popular options,low carbon travel intention is a comprehensive result of different factors,and the"celebrity effect"has a significant impact on low-carbon travel intentions.The conclusions of this study will aid in improving low-carbon travel policies.
low-carbon travelsocial media sentimenttext miningtopic analysis
万方数据
维普
