Abstract:
"
Social media platforms are a vast area of today's world, and social media are mainly
focusing on business models and advertising. There are many researchers contributed
towards social media stress releasing, hate speech detection like that. In this research,
finding the gap between vulnerable peoples who are in different regions and different
countries. This was considered as a problem, for looking them to accumulate in one
platform and prevent them from hate speech and unwanted disturbance from social
media. The research also demonstrates about creating separate platforms to make them
in a selective group Using their Challenges and vulnerabilities and type of diseases.
Designing and implementing a community classifier using users’ description and
deriving key features from the text and analyzing those in a deep learning model do
suggest suitable communities. The main component is to provide a community name
for the user according to the description given for the system while on boarding.
Developing community classifiers which contain several types of tasks, and one of the
biggest tasks is to create a RNN model using different types of diseases and their
symptoms. The biggest task is to collect details and using an NLP remove unwanted
words and creator collections of keywords and train them using RNN. The
implemented system detecting the category of diseases with more accuracy end very
quick responsive way .And the addition of Core-feature there will be a chat board to
have discussion with the group block of people"
