This study proposes a novel method for sentiment analysis of social media data, specifically X (formerly known as Twitter) data, and demonstrates its efficacy for predicting sentiments related to environmental issues. The proposed method employed state-of-the-art algorithms and machine learning approaches to perform the sentiment analysis of posts pertaining to environmental challenges in Malaysia, a country with significant social media usage in the Asia-Pacific region. The results showed that most of the posts analyzed were neutral in sentiment, suggesting a less polarized discourse on environmental challenges in Malaysia. The findings highlight the need for policy changes and environmental education to promote concern for environmental challenges and pro-environmental behavior among Malaysian residents. The proposed method is simple to use and accurately predicted sentiment from the X data. In addition to providing a valuable tool for researchers, the method has the potential to advance the field of sentiment analysis of social media data and be replicated in future research and practice.