Review of 'Public Opinion Analysis 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.