Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory a...Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.展开更多
Bus and any other public transit connectivity issues facilitate an understanding of the importance of transit planning in enhancing existing or new transit services. Improving transit connectivity is one of the most v...Bus and any other public transit connectivity issues facilitate an understanding of the importance of transit planning in enhancing existing or new transit services. Improving transit connectivity is one of the most vital tasks in transit-operations planning. A poor connection can cause some passengers to stop using the transit service. Service-design criteria always contain postulates to improve routing and scheduling coordination （intra- and inter-agency transfer centers/points and synchronized/timed transfers）. Ostensibly the lack of well-defined connectivity measures precludes the weighing and quantifying of the result of any coordination effort. This work provides an initial methodological framework and concepts for （1） quantifying transit connectivity measures and （2） directions and tools for detecting weak segments in inter-route and inter-modal chains （paths） for possible revisions/changes.展开更多
Large-scale public buildings have high energy density, which on average consume 5 to 15 times more electricity than residential buildings. In Beijing, those public buildings account for about ten percent of the total ...Large-scale public buildings have high energy density, which on average consume 5 to 15 times more electricity than residential buildings. In Beijing, those public buildings account for about ten percent of the total building area, but their energy consumption （except heating） amounts to more than thirty percent of the total. Few electric meters are installed in those public buildings, however, making it more difficult to monitor how the energy is used.展开更多
基金This paper is supported by the National Natural Science Foundation of China under contract No.71774084,72274096the National Social Science Fund of China under contract No.16ZDA224,17ZDA291.
文摘Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.
文摘Bus and any other public transit connectivity issues facilitate an understanding of the importance of transit planning in enhancing existing or new transit services. Improving transit connectivity is one of the most vital tasks in transit-operations planning. A poor connection can cause some passengers to stop using the transit service. Service-design criteria always contain postulates to improve routing and scheduling coordination （intra- and inter-agency transfer centers/points and synchronized/timed transfers）. Ostensibly the lack of well-defined connectivity measures precludes the weighing and quantifying of the result of any coordination effort. This work provides an initial methodological framework and concepts for （1） quantifying transit connectivity measures and （2） directions and tools for detecting weak segments in inter-route and inter-modal chains （paths） for possible revisions/changes.
文摘Large-scale public buildings have high energy density, which on average consume 5 to 15 times more electricity than residential buildings. In Beijing, those public buildings account for about ten percent of the total building area, but their energy consumption （except heating） amounts to more than thirty percent of the total. Few electric meters are installed in those public buildings, however, making it more difficult to monitor how the energy is used.
