  Suicide and suicidal behaviors remain significant challenges for public
policy and healthcare. In response, psychological support hotlines have been
established worldwide to provide immediate help to individuals in mental
crises. The effectiveness of these hotlines largely depends on accurately
identifying callers' emotional states, particularly underlying negative
emotions indicative of increased suicide risk. However, the high demand for
psychological interventions often results in a shortage of professional
operators, highlighting the need for an effective speech emotion recognition
model. This model would automatically detect and analyze callers' emotions,
facilitating integration into hotline services. Additionally, it would enable
large-scale data analysis of psychological support hotline interactions to
explore psychological phenomena and behaviors across populations. Our study
utilizes data from the Beijing psychological support hotline, the largest
suicide hotline in China. We analyzed speech data from 105 callers containing
20,630 segments and categorized them into 11 types of negative emotions. We
developed a negative emotion recognition model and a fine-grained multi-label
classification model using a large-scale pre-trained model. Our experiments
indicate that the negative emotion recognition model achieves a maximum
F1-score of 76.96%. However, it shows limited efficacy in the fine-grained
multi-label classification task, with the best model achieving only a 41.74%
weighted F1-score. We conducted an error analysis for this task, discussed
potential future improvements, and considered the clinical application
possibilities of our study. All the codes are public available.
