Average rating: | Rated 3.5 of 5. |
Level of importance: | Rated 3 of 5. |
Level of validity: | Rated 3 of 5. |
Level of completeness: | Rated 3 of 5. |
Level of comprehensibility: | Rated 4 of 5. |
Competing interests: | None |
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.