Economic sentiment plays a central role in shaping perceptions of economic conditions, influencing economic behavior, and policy decisions. Recent advances in large language models have made large-scale sentiment extraction from news increasingly feasible, driving its growing use in economic analysis and forecasting systems across national public-sector institutions. Existing approaches scale mainly through automated or weakly supervised labeling, sacrificing expert alignment and domain nuance, and treat sentiment in isolation, overlooking complementary annotations, such as topic, needed to capture structured economic signals for downstream forecasting. To address these limitations, we introduce a human-annotated dataset of Italian economic news labeled by domain experts for sentiment and topic, and develop a statistically validated pipeline that uses few-shot prompting of commercial large language models to expand coverage while preserving expert alignment at scale. We fine-tune multiple open-weight models on the resulting corpus, yielding language specific annotation models that maintain high expert agreement across architectures and satisfy the reproducibility and governance requirements of national public-sector institutions. Starting from 800 expert-labeled articles, the pipeline produces approximately 9,200 additional expert-aligned labels and trains local models that pass the Alternative Annotator Test for both sentiment and topic classification. A subset of the annotated dataset is publicly released to support further research.