| Sumario: | Simple Summary: Many people with cancer experience anxiety or depression, but routine emotional screening can be difficult in busy oncology clinics. In this study, 165 adult cancer outpatients completed the Hospital Anxiety and Depression Scale and also wrote a short Turkish free-text statement about how they felt. The written responses were analyzed with an artificial intelligence language model that produced a sentiment score for each patient, reflecting whether the emotional tone was more negative or more positive. More positive sentiment scores were associated with lower anxiety and depression scores, while female patients had higher distress scores. Clinical factors such as cancer stage, diagnosis type, treatment status, and time since diagnosis were not independently linked with distress. These findings suggest that brief patient-written texts, analyzed automatically, may help clinicians identify emotional distress and could complement standard psychological screening in routine oncology care. Background: Psychological distress, particularly anxiety and depression, is highly prevalent among cancer patients, and is associated with impaired quality of life, reduced treatment adherence, and increased mortality risk. Standardized screening instruments, such as the Hospital Anxiety and Depression Scale (HADS), are effective, but face implementation barriers in busy oncology outpatient settings. This cross-sectional study investigated whether BERT-based Natural Language Processing (NLP) analysis of brief patient-generated free texts would correlate with HADS scores in a consecutive cohort of cancer outpatients. Material and Methods: A total of 165 consecutive adult cancer outpatients were enrolled at a tertiary oncology center in Turkey. All participants completed the HADS questionnaire and were asked to write freely about their current emotional state in Turkish. Patient-generated texts were analyzed using a pre-trained Turkish BERT model to derive a continuous BERT Sentiment Score (BSS) and a categorical BERT Sentiment Cluster (BSC) via unsupervised hierarchical clustering. Univariate and multivariate linear regression analyses were performed to examine associations between clinical, demographic, and NLP-derived variables and the logarithmically transformed HADS score. Results: The mean total HADS score was 10.46 (range, 0–33), consistent with a moderate level of psychological distress. In multivariate analysis, two variables were independently associated with HADS scores: female sex (β = 0.20, t = 2.14, p = 0.034), associated with higher HADS scores, and BERT Sentiment Score (BSS) (β = −0.18, t = −2.43, p = 0.016), with higher values corresponding to lower HADS scores. Hierarchical clustering identified two distinct thematic groups: 'Coping and Fighting Spirit' (74%), and 'Hope and Negative Feelings' (26%); however, cluster membership (BSC) was not independently associated with HADS scores (β = −0.02, p = 0.789). Clinical variables, including cancer stage, diagnosis type, treatment status, and time since diagnosis, also were not independently associated with HADS scores. Conclusions: BERT-based sentiment analysis of brief patient-generated free texts yielded a continuous measure that independently correlated with HADS scores in cancer outpatients, alongside female sex. These findings provide proof-of-concept evidence that NLP-derived sentiment scoring may offer a practical, scalable, and complementary approach to standardized psychological screening in routine oncology care.
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