  Depression is a mental health disorder that has a profound impact on people's
lives. Recent research suggests that signs of depression can be detected in the
way individuals communicate, both through spoken words and written texts. In
particular, social media posts are a rich and convenient text source that we
may examine for depressive symptoms. The Beck Depression Inventory (BDI)
Questionnaire, which is frequently used to gauge the severity of depression, is
one instrument that can aid in this study. We can narrow our study to only
those symptoms since each BDI question is linked to a particular depressive
symptom. It's important to remember that not everyone with depression exhibits
all symptoms at once, but rather a combination of them. Therefore, it is
extremely useful to be able to determine if a sentence or a piece of
user-generated content is pertinent to a certain condition. With this in mind,
the eRisk 2023 Task 1 was designed to do exactly that: assess the relevance of
different sentences to the symptoms of depression as outlined in the BDI
questionnaire. This report is all about how our team, Mason-NLP, participated
in this subtask, which involved identifying sentences related to different
depression symptoms. We used a deep learning approach that incorporated
MentalBERT, RoBERTa, and LSTM. Despite our efforts, the evaluation results were
lower than expected, underscoring the challenges inherent in ranking sentences
from an extensive dataset about depression, which necessitates both appropriate
methodological choices and significant computational resources. We anticipate
that future iterations of this shared task will yield improved results as our
understanding and techniques evolve.
