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This article explores opinions and semantic orientation around fertility and parenthood by scrutinizing filtered Italian Twitter data. We propose a novel methodological framework relying on Natural Language Processing techniques for text analysis and social media corpora development, which is aimed at extracting sentiments from texts. A multi-layered manual annotation for exploring sentiment and attitudes to fertility and parenthood was applied to Twitter data. The corpus was analysed through sentiment and emotion lexicons in order to highlight how affective language is used in this domain. It emerges that parents express a generally positive attitude towards children, while children are more critical towards parents. The corpus constitutes a first step to improve our understanding of attitudes towards fertility and parenthood in this kind of contents.