Abstract:
Depression is viewed as the largest contributor lo global disability and a major reason
for suicide. It has an impact on the language usage reflected in the written text. The
key objective of our study is to examine Redd it users’ posts to detect any factors that
may reveal the depression attitudes of relevant online users. For such purpose, we
employ the Natural Language Processing (NLP) techniques and machine learning
approaches to train the data and evaluate the efficiency of our proposed method. We
identify a lexicon ofterms that are more common among depressed accounts.
This application utilizes the RNN strategies to build up the application. The neural
conversation model can cater to a variety of requests, as it generates the responses
word by word as opposed to using canned responses. The hybrid system shows
significant improvements over the existing baseline system ofrule-based approach and
caters to complex queries with a domain-restricted neural model. Many various models
ofNN are examined, and LSTM calculation was utilized in this application as its fit
for shaping portrayals ofthe highlights which is inward in order.
Automatic detection of depression has attracted increasing attention from researchers
in psychology, computer science, linguistics, and related disciplines. As a result,
promising depression detection systems have been reported. This paper surveys these
efforts by presenting the first cross-modal review ofdepression detection systems and
discusses best practices and most promising approaches to this task. This framework
is intended to tweak the organization for an individual client. Proposals for future turn
of events and ends are additionally remembered for the report.
