Abstract
Everyone may now readily communicate and share their sentiments with
individuals around the world thanks to the various social networking
platforms. In the current scenario, some people are more extroverted on
social media than others around them. In the end, whenever they get into
trouble or any situation where they need other people’s help who gives
them motivation or who cares, they would not be there because that time
sufferer prefers to express their feeling on social media rather than
any close once. Therefore, if they are warned in advance, there are some
strategies that reduce the stress and mental health issues that they
are experiencing. Due to rising these issues globally, attract many
researchers to focus on the subject and provide some viable solutions,
where there is still a need for more research that provides some
efficient results. So finally, we built a project in which it takes
users’ tweets as input, and it will give the result in the form of
depression or not using the help of a machine learning algorithm. For
the segmentation of tweets, we are using LSTM (Long Short-Term Memory)
machine learning algorithm which is best suited for this task. Not only
this but LSTM performs best out of available machine learning
algorithms. All the emotions are identified as neutral, positive, or
negative which assists to provide a solution toward sentiments. Apart
from this, we are not focusing on only the English language but we will
try to counter this issue in different languages by translating other
languages into English, which other researchers have missed out on in
their research. With the help of these results, the company will take
different steps to mitigate the stress from those users by providing
motivational feeds in their feed section or any other way.