We present a study investigating the linguistic sentiment associated with schizophrenia and depression in research-based texts. To this end, we construct a corpus of over 260,000 PubMed abstracts published between 1975 and 2025, covering both disorders. For sentiment analysis, we fine-tune two sentence-transformer models using SetFit with a training dataset consisting of sentences rated for valence by psychiatrists and clinical psychologists. Our analysis identifies significant temporal trends and differences between the two conditions. While the mean positive sentiment in abstracts and titles increases over time, a more detailed analysis reveals a marked rise in both maximum negative and maximum positive sentiment, suggesting a shift toward more polarized language. Notably, sentiment in abstracts on schizophrenia is significantly more negative overall. Furthermore, an exploratory analysis indicates that negative sentences are disproportionately concentrated at the beginning of abstracts. These findings suggest that linguistic style in scientific literature is evolving. We discuss the broader ethical and societal implications of these results and propose recommendations for more cautious language use in scientific discourse.