Mood disorders, such as depression, manifest in various psychological and physical symptoms. Persistent feelings of sadness can disrupt daily functioning, while accompanying issues like insomnia and loss of appetite further exacerbate the condition. If left untreated, depression can lead to a range of complications, including severe illnesses and potentially life-threatening outcomes. In recent years, there has been a notable rise in psychiatric disorder cases, with a significant increase observed in mood disorders, particularly depression. Many individuals either fail to recognize their symptoms or are hesitant to seek professional help. As a result, only a fraction of affected individuals receive adequate treatment. This paper proposes a method for estimating depressive tendencies by analyzing observable data, such as conversational content between users and chatbots, as well as their usage patterns. By leveraging these insights, we aim to improve early detection and intervention strategies for individuals at risk of depression.
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