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    Review of 'Public Opinion Analysis on Social Media Platforms: A Case Study of High Speed 2 (HS2) Rail Infrastructure Project'

    Public Opinion Analysis on Social Media Platforms: A Case Study of High Speed 2 (HS2) Rail Infrastructure ProjectCrossref
    Well written paper
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        Rated 3.5 of 5.
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        Rated 3 of 5.
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        Rated 3 of 5.
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        Rated 4 of 5.
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    Public Opinion Analysis on Social Media Platforms: A Case Study of High Speed 2 (HS2) Rail Infrastructure Project

    Abstract: Public opinion evaluation is becoming increasingly significant in infrastructure project assessment. The inefficiencies of conventional evaluation approaches can be improved with social media analysis. Posts about infrastructure projects on social media provide a large amount of data for assessing public opinion. This study proposed a public opinion evaluation framework with machine learning algorithms, including sentiment analysis and topic modelling. We selected the United Kingdom railway project, High Speed 2, as the case study. The sentiment analysis showed that around 53% to 63% of tweets expressed a negative sentiment, suggesting the public may have an overall negative perception of the project. Topic modelling with text corpora showed key topics of public opinion. The proposed framework demonstrates the feasibility of using supervised machine learning to evaluate public opinion on infrastructure projects, as the framework can save time and cost. Furthermore, assessment results can aid policymakers and managers in decision-making.

      Review information

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

      Engineering,Civil engineering
      Public opinion evaluation,Sentiment analysis,Transport,Policy and law,Environmental policy and practice,Machine learning,Civil infrastructure projects,Topic modelling,Sustainability

      Review text

      The paper investigates sentiment analysis and topic modelling using machine learning based on Twitter data. It considers a specific topic related to the UK railway project, High Speed 2. The paper compares Multinomial Naïve Bayes and Support Vector Machine for sentiment analysis of tweets. Topic modelling was conducted with Latent Dirichlet Allocation (LDA) using publicly available scripts. Experiments, discussion, and results are presented. The paper is written well, and sufficient background is included. The references are appropriate but some more recent ones could have been included. The paper provides insights into the feasibility of using social media data for public opinion evaluation of civil infrastructure projects. The study's contribution lies in presenting a public opinion evaluation framework with a machine learning algorithm and comparing the accuracy of two classifiers.



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