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

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 hybrid model which combines pre-trained RoBERTa and gated recurrent units for sentiment analysis. We selected the United Kingdom railway project, High Speed 2 (HS2), as the case study. The sentiment analysis showed the proposed hybrid model has good performance in classifying social media sentiment. Furthermore, the study applies latent Dirichlet allocation topic modelling to identify key themes within the tweet corpus, providing deeper insights into the prominent topics surrounding the HS2 project. The findings from this case study serve as the basis for a comprehensive public opinion evaluation framework driven by social media data. This framework offers policymakers a valuable tool to effectively assess and analyse public sentiment

Predicting serious rare adverse reactions of novel chemicals

Motivation: Adverse drug reactions (ADRs) are one of the main causes of death and a major financial burden on the world\u27s economy. Due to the limitations of the animal model, computational prediction of serious and rare ADRs is invaluable. However, current state-of-the-art computational methods do not yield significantly better predictions of rare ADRs than random guessing. Results: We present a novel method, based on the theory of \u27compressed sensing\u27 (CS), which can accurately predict serious side-effects of candidate and market drugs. Not only is our method able to infer new chemical-ADR associations using existing noisy, biased and incomplete databases, but our data also demonstrate that the accuracy of CS in predicting a serious ADR for a candidate drug increases with increasing knowledge of other ADRs associated with the drug. In practice, this means that as the candidate drug moves up the different stages of clinical trials, the prediction accuracy of our method will increase accordingly. Availability and implementation: The program is available at https://github.com/poleksic/side-effects. Supplementary information: Supplementary data are available at Bioinformatics online

Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation

Adverse drug reaction (ADR) is a major burden for patients and healthcare industry. It usually causes preventable hospitalizations and deaths, while associated with a huge amount of cost. Traditional preclinical in vitro safety profiling and clinical safety trials are restricted in terms of small scale, long duration, huge financial costs and limited statistical signifi- cance. The availability of large amounts of drug and ADR data potentially allows ADR predictions during the drugs’ early preclinical stage with data analytics methods to inform more targeted clinical safety tests. Despite their initial success, existing methods have trade-offs among interpretability, predictive power and efficiency. This urges us to explore methods that could have all these strengths and provide practical solutions for real world ADR predictions. We cast the ADR-drug relation structure into a three-layer hierarchical Bayesian model. We interpret each ADR as a symbolic word and apply latent Dirichlet allocation (LDA) to learn topics that may represent certain biochemical mechanism that relates ADRs with drug structures. Based on LDA, we designed an equivalent regularization term to incorporate the hierarchical ADR domain knowledge. Finally, we developed a mixed input model leveraging a fast collapsed Gibbs sampling method that the complexity of each iteration of Gibbs sampling proportional only to the number of positive ADRs. Experiments on real world data show our models achieved higher prediction accuracy and shorter running time than the state-of-the-art alternatives