Environmental, Social, and Governance (ESG) reporting and proper measurement of greenhouse gas emissions are becoming increasingly important for industries with substantial environmental impact. This research aims to assess the current state of ESG reporting practices and highlight areas for improvement across the automotive, construction and energy industries operating in the Central Eastern European (CEE) region. To achieve this aim, a multi-industry sustainability disclosure database was created and analyzed through a Python-based text-mining methodology, using term frequency-inverse document frequency and keyword-in-context analysis. The process involved extracting and preprocessing text from 60 sustainability reports for the year 2021, followed by constructing a custom dictionary of key ESG terms aligned with the European Sustainability Reporting Standards. The findings reveal considerable variance in the focus of qualitative disclosures across industries, particularly regarding climate change and biodiversity. The investigation underscores the need for enhanced transparency, consistent metrics, and rigorous validation in ESG reporting. The study also provides new insights into the technical possibilities of automated text analysis for sustainability reporting in the CEE region, and highlights key areas where improvement appears necessary.