Analyzing public sentiment on sustainability: A comprehensive review and application of sentiment analysis techniques

Abstract

In the contemporary context of escalating environmental concerns, understanding public sentiment toward sustainability initiatives is crucial for shaping effective policies and practices. This research explores the efficacy of sentiment analysis in mining social media data to gauge public attitudes toward sustainability efforts. This study employs a variety of machine learning and deep learning models to perform sentiment analysis utilizing a dataset comprising tweets related to human perception towards environmental sustainability. The aim is to transform unstructured social media text into structured sentiment scores. The comparative analysis includes pre-trained sentiment analysis models like VADER, TextBlob, and Flair with traditional machine learning models such as Logistic Regression, SVM, Decision Tree, Naive Bayes, Random Forest, alongside advanced deep learning techniques like LSTM and pre-trained models BERT and GPT-2. Our results reveal significant variations in model performance, underscoring the importance of selecting appropriate sentiment analysis tools that align with the nuanced domain of sustainability. The study further emphasizes the role of transparent and reproducible research practices in advancing trustworthy AI applications. By providing insights into public opinions on sustainability, this research contributes to the broader discourse on leveraging AI to foster environmental responsibility and action. This work not only illustrates the potential of sentiment analysis in understanding public discourse but also highlights the critical need for tailored approaches that consider the specificity of the sustainability context.