Public opinion evaluation on social media platforms: a case study of High Speed 2 (HS2) rail infrastructure project

Summary:
In this study, the authors successfull conduct an analysis of public opinions on a public infrastructure project (the High Speed 2 railway project in the United Kingdom) using data from Twitter and machine learng algorithms. The approach is sound, but the methodology could do with the adoption of SOTA models, benchmarking against very basic models, and handling potentially imbalanced datasets. Details are provided below:

1. Deep learning techniques, especially those that employ transformer architectures are the current SOTA. While methods like Naive Baye, SVM’s and LDA are still very useful, it would be prudent to compare with the results from transformer-based deep learning architectures. Neural networks in the transformer family fine-tuned for specific tasks like classification have proven to be a very promising research direction in recent years, and some models like twitter-roberta-base-sentiment can be used out of the box. Since these deep learning architectures transform text into numerical embeddings that preserve semantic context to a degree, they reduce the amount of pre-processing that has to be done on tweets (like stemming and stop-word removal).
2. Another good tool to consider for establishing baselines is the VADER sentiment analysis model, which was developed specifically for social media use-cases. In the worst case it can serve as a reasonable baseline, since it requires no training.
3. Steps should be taken to address dataset imbalance. If any such steps were taken, they were not stated. This can cause issues for a classifier, such as overfitting to a label with an overwhelmingly higher representaion. The F1 score is a good metric for catching this, but it might be better to train on a balanced dataset.

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.

Summary: in this work, the author proposed a learning-based framework to evaluate public opinion through social media platform. Most of the contents are presented clearly with a proper use of language. The methods effectively solve the problem, and the experimental results are acceptable.

Comments.
1, there are symbols not correct in math formulas. In Eq.1), the x_1 and p(xn|y) are not in correct form.
2, the state-of-the-art methods are not clearly stated and the comparison between the proposed method and the SOTA is not verified. What is the significance of this work over previous work?
3, in section 3.2.2, on what machine do you train your model and what is the time consumption?
4, the training results in section 3.2.2 is not very good. Have you consider using deep neural networks to solve the problem?
5, how to evaluate the accuracy of sentiment analysis and topic modeling?