Sentiment Analysis of Pro-Environmental Behavior Using X Data: A Comparative Study of Waste Management in Spain and Macedonia

Public engagement is a cornerstone of effective waste management, yet regional attitudes toward environmental practices remain underexplored. Social media provides critical insights into evolving public discourse, particularly for sustainability initiatives where community perception drives policy success. This research examines pro-environmental behavior through sentiment analysis of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> (Twitter) data, focusing on waste management discussions in Spain and Macedonia from May 2024 to October 2024. We employ BERT-based models Multilingual BERT and BERTweet to analyze sentiment and perform topic modeling using BERTopic. For Spanish tweets, native-language analysis with Multilingual BERT outperforms BERTweet when tested on translated data, revealing a predominantly positive sentiment (80%). In contrast, Macedonian tweets, translated into English and evaluated on both models, show consistent performance but reflect an opposite trend, with 70% of tweets expressing negative sentiment. Sentiment analysis categorizes tweets into positive, neutral, and negative classes, enabling a comparative evaluation of model effectiveness across translations and languages. Additionally, BERTopic identifies prominent themes across the dataset and within sentiment categories, offering insights into public attitudes and discussions. These findings reveal regional differences in waste management perceptions and their importance for sentiment and topic analysis, aiding environmental communication insights.