Great Wall Motor(GWM),a leading automotive manufacturer,places a strong emphasis on environmental sustainability and social responsibility.The company focuses on comprehensively evaluating and enhancing its supply cha...Great Wall Motor(GWM),a leading automotive manufacturer,places a strong emphasis on environmental sustainability and social responsibility.The company focuses on comprehensively evaluating and enhancing its supply chain to align with these objectives.This evaluation spans the entire product life cycle,encompassing design,manufacturing,packaging,distribution,usage,and recycling and disposal processes.Key areas of focus include optimizing raw material selection,improving product recyclability,reducing energy consumption and waste emissions,and minimizing carbon emissions during transportation.Through these endeavors,GWM not only enhances its environmental performance by reducing carbon emissions and resource consumption but also bolsters its brand image and competitiveness in the market.GWM’s dedication to environmental innovation and technological leadership serves as a driving force behind sustainable development and social responsibility within the industry.展开更多
There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,w...There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,we explore the dependence among relevant posts via authors'backgrounds,since the authors with similar backgrounds,e.g.,"gender","location",tend to express similar emotions.However,personal attributes are not easy to obtain in most social media websites.Accordingly,we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection:the Joint Model with Personal Attention Mechanism(JPA)model is used to detect emotion and personal attributes jointly,and capture the attributes-aware words to connect similar people;the Neural Personal Discrimination(NPD)model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection.Experimental results show the usefulness of personal attributes in emotion detection,and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.展开更多
文摘Great Wall Motor(GWM),a leading automotive manufacturer,places a strong emphasis on environmental sustainability and social responsibility.The company focuses on comprehensively evaluating and enhancing its supply chain to align with these objectives.This evaluation spans the entire product life cycle,encompassing design,manufacturing,packaging,distribution,usage,and recycling and disposal processes.Key areas of focus include optimizing raw material selection,improving product recyclability,reducing energy consumption and waste emissions,and minimizing carbon emissions during transportation.Through these endeavors,GWM not only enhances its environmental performance by reducing carbon emissions and resource consumption but also bolsters its brand image and competitiveness in the market.GWM’s dedication to environmental innovation and technological leadership serves as a driving force behind sustainable development and social responsibility within the industry.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos.62176174 and 61806137.
文摘There has been a recent line of work to automatically detect the emotions of posts in social media.In literature,studies treat posts independently and detect their emotions separately.Different from previous studies,we explore the dependence among relevant posts via authors'backgrounds,since the authors with similar backgrounds,e.g.,"gender","location",tend to express similar emotions.However,personal attributes are not easy to obtain in most social media websites.Accordingly,we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection:the Joint Model with Personal Attention Mechanism(JPA)model is used to detect emotion and personal attributes jointly,and capture the attributes-aware words to connect similar people;the Neural Personal Discrimination(NPD)model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection.Experimental results show the usefulness of personal attributes in emotion detection,and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.
