Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured thro...Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.展开更多
With the development of Web 2.0,more and more people choose to use the Internet to express their opinions.All this opinions together into a new form text which contains a lot of valuable emotional information,this is ...With the development of Web 2.0,more and more people choose to use the Internet to express their opinions.All this opinions together into a new form text which contains a lot of valuable emotional information,this is why how to deal with these texts and analysis the emotional information is significant for us.We get three main tasks of sentiment analysis,including sentiment extraction,sentiment classification,sentiment application and summarization.In this paper,based on the R software,we introduced the steps of sentiment analysis in detail.Finally,we collect the movie reviews from the Internet,and use R software to do sentiment analysis in order to judge the emotional tendency of the text.展开更多
With the gradual advancement of digital transformation in the tourism industry,exploiting implicit information of tourism demand for prediction has gradually become mainstream in this research field.Among these works,...With the gradual advancement of digital transformation in the tourism industry,exploiting implicit information of tourism demand for prediction has gradually become mainstream in this research field.Among these works,the research on unidirectional implicit information is well-developed,whereas studies on interactive implicit information are scarce.Therefore,to further enhance the performance of tourism forecasting,this study employs a LangChain-based interactive context sentiment analysis model in the realm of feature engineering.By incorporating the sentiment tendencies found in online tourism reviews into tourism demand forecasting research,the model's inferential capabilities are significantly improved.In terms of model processing,a new tourism demand prediction fusion model,EMD-STGCN-GRU-LSTM-Transformer(abbreviated as EST-Net),has been developed to address the unique spatio-temporal characteristics and imbalance of tourism data,thereby enhancing the model's ability to accurately extract spatio-temporal sequences.Additionally,the PCA method is utilized to aggregate multiple key indicators of sentiment attention and natural environmental factors,constructing a tourism prediction indicator system to further correct the overall framework bias.展开更多
基金supported by the National Nature Foundation of China under Grants(No.72104108)the College Students’Innovation and Entrepreneurship Training Program(No.202410298155Y).
文摘Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.
文摘With the development of Web 2.0,more and more people choose to use the Internet to express their opinions.All this opinions together into a new form text which contains a lot of valuable emotional information,this is why how to deal with these texts and analysis the emotional information is significant for us.We get three main tasks of sentiment analysis,including sentiment extraction,sentiment classification,sentiment application and summarization.In this paper,based on the R software,we introduced the steps of sentiment analysis in detail.Finally,we collect the movie reviews from the Internet,and use R software to do sentiment analysis in order to judge the emotional tendency of the text.
基金Supported by the National Social Science Fund of China(24BJY088)the Natural Science Foundation of Guangdong Province(2025A1515011633)。
文摘With the gradual advancement of digital transformation in the tourism industry,exploiting implicit information of tourism demand for prediction has gradually become mainstream in this research field.Among these works,the research on unidirectional implicit information is well-developed,whereas studies on interactive implicit information are scarce.Therefore,to further enhance the performance of tourism forecasting,this study employs a LangChain-based interactive context sentiment analysis model in the realm of feature engineering.By incorporating the sentiment tendencies found in online tourism reviews into tourism demand forecasting research,the model's inferential capabilities are significantly improved.In terms of model processing,a new tourism demand prediction fusion model,EMD-STGCN-GRU-LSTM-Transformer(abbreviated as EST-Net),has been developed to address the unique spatio-temporal characteristics and imbalance of tourism data,thereby enhancing the model's ability to accurately extract spatio-temporal sequences.Additionally,the PCA method is utilized to aggregate multiple key indicators of sentiment attention and natural environmental factors,constructing a tourism prediction indicator system to further correct the overall framework bias.
