A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Online Public Opinion Analysis on Infrastructure Megaprojects: Toward an Analytical Framework
The development of an infrastructure megaproject is closely related to society and the public community. It is necessary to pay enough attention to public opinions that generally have a significant impact on megainfrastructure’s performance. As the longest sea-crossing bridge in the world, the Hong Kong–Zhuhai–Macao Bridge (HZMB) receives extensive attention from both the industry of bridge construction and the general public. Every individual is a potential user of this megaproject. Ignorance of public opinion may inhibit the final success of the HZMB. Based on two dimensions of stage and region, this study aims to devise an analytical framework for topic modeling and sentiment analysis of the megainfrastructure in the data-rich era. Latent Dirichlet allocation (LDA) as a flexible generative probabilistic model was adopted for topic extraction, and the measure of perplexity was used for determining the optimal number of topics in every stage or region. Rule-based sentiment analysis was conducted for identifying sentiment polarity and calculating sentiment intensity values of the validated data set. The results denoted that topics varied in four stages and three regions directly connected to the bridge. Positive comments occupied the largest proportion in every stage or region. According to sentiment polarity and intensity, the proposed approach for sentiment analysis had a higher ability to recognize positive comments from four stages and three regions. This study contributes to public opinion analysis of megainfrastructures within the context of sufficient social media data, which provides new opportunities for data-driven infrastructure management and governance. Management recommendations can be obtained to guide similar infrastructure’s public opinion management in future.
Online Public Opinion Analysis on Infrastructure Megaprojects: Toward an Analytical Framework
The development of an infrastructure megaproject is closely related to society and the public community. It is necessary to pay enough attention to public opinions that generally have a significant impact on megainfrastructure’s performance. As the longest sea-crossing bridge in the world, the Hong Kong–Zhuhai–Macao Bridge (HZMB) receives extensive attention from both the industry of bridge construction and the general public. Every individual is a potential user of this megaproject. Ignorance of public opinion may inhibit the final success of the HZMB. Based on two dimensions of stage and region, this study aims to devise an analytical framework for topic modeling and sentiment analysis of the megainfrastructure in the data-rich era. Latent Dirichlet allocation (LDA) as a flexible generative probabilistic model was adopted for topic extraction, and the measure of perplexity was used for determining the optimal number of topics in every stage or region. Rule-based sentiment analysis was conducted for identifying sentiment polarity and calculating sentiment intensity values of the validated data set. The results denoted that topics varied in four stages and three regions directly connected to the bridge. Positive comments occupied the largest proportion in every stage or region. According to sentiment polarity and intensity, the proposed approach for sentiment analysis had a higher ability to recognize positive comments from four stages and three regions. This study contributes to public opinion analysis of megainfrastructures within the context of sufficient social media data, which provides new opportunities for data-driven infrastructure management and governance. Management recommendations can be obtained to guide similar infrastructure’s public opinion management in future.
Online Public Opinion Analysis on Infrastructure Megaprojects: Toward an Analytical Framework
Zhou, Zhipeng (author) / Zhou, Xingnan (author) / Qian, Lingfei (author)
2020-11-09
Article (Journal)
Electronic Resource
Unknown
| Online Contents | 2007
| British Library Online Contents | 2009
| TIBKAT | 2023
| Online Contents | 2009