Recent studies explore the intersection of natural language processing (NLP) and mental health, focusing on early maladaptive schemas and depression detection. Gollapalli et al. (2023) discuss identifying maladaptive schemas through mental health questions, while Zhou et al. (2023) validate a language model to understand circumstances around female firearm suicides. Olteanu et al. (2019) examine biases inherent in social data, alongside research from Navigli et al. (2023) that addresses biases in large language models. Evaluating these models’ capabilities, Chang et al. (2023) provide insights into performance measurement. Jin et al. (2025) perform a scoping review on NLP applications in mental health, indicating growing relevance. Notably, tools like BERT and advanced speech recognition systems are emerging for accurate depression detection, as shown in multiple studies (e.g., Wu et al., 2023; Qasim et al., 2025). These advancements highlight the potential of AI in improving mental health assessment, though ethical considerations remain paramount.
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