Abstract
Social media mining has proven useful in multiple research fields as a tool for public opinion extraction and analysis. Such mining can discover knowledge from unstructured data in booming social media sources that provide instant public responses and also capture long-term data. Environmental scientists have realized its potential and conducted various studies where public opinion matters. We focus our discussion in this article on mining social media text on environmental issues, with particular emphasis on sentiment analysis, fitting the theme of Data Science and Sustainability. The data science community today is interested in topics that overlap with environmental issues and their broader impacts on sustainability. Such work appeals to scientists focusing on areas such as smart cities, climate change and geo-informatics. Future issues emerging from this research include domain-specific multilingual mining, and advanced geo-location tagging with demographically focused sentiment analysis.
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