This study presents a comprehensive computational analysis of a curated dataset of instruction–response pairs within the sustainable fashion domain. The dataset comprises consumer-style queries and expert-style answers, providing a dual perspective view of sustainability discourse in fashion. Using a multi-stage text mining pipeline, we examine linguistic properties, sentiment polarity, sustainability depth, inclusivity attributes, and thematic structures through non-negative matrix factorization (NMF) topic modeling. Sentiment analysis is implemented using a domain-adapted lexicon to capture tone differences between queries and responses, while sustainability depth scoring evaluates the presence of critical concepts such as material choice, care practices, circular economy principles, ethical sourcing, and environmental impact. Inclusivity is assessed through rule-based detection of body-positive, gender-neutral, cultural, and sensory accessibility references. The results reveal a marked positivity bias in responses compared to instructions, with a consistent tone shift towards encouragement and reassurance. Topic modeling identifies ten recurring themes, including materials and fabrics, care and longevity, capsule wardrobe planning, ethics and sourcing, and seasonal dressing. Sustainability depth is highest in material-focused and ethics-related topics, whereas styling and fit-oriented topics show lower integration of environmental principles. Inclusivity coverage is uneven, with cultural and gender-neutral aspects underrepresented. The findings are interpreted in the context of strategic brand communication, consumer education, and policy advocacy.