  The most common mental disorders experienced by a person in daily life are
depression and anxiety. Social stigma makes people with depression and anxiety
neglected by their surroundings. Therefore, they turn to social media like
Twitter for support. Detecting users with potential depression and anxiety
disorders through textual data is not easy because they do not explicitly
discuss their mental state. It takes a model that can identify potential users
who experience depression and anxiety on textual data to get treatment earlier.
Text classification techniques can achieve this. One approach that can be used
is LSTM as an RNN architecture development in dealing with vanishing gradient
problems. Standard LSTM does not capture enough information because it can only
read sentences from one direction. Meanwhile, Bidirectional LSTM (BiLSTM) is a
two-way LSTM that can capture information without ignoring the context and
meaning of a sentence. The proposed BiLSTM model is higher than all traditional
machine learning models and standard LSTMs. Based on the test results, the
highest accuracy obtained by BiLSTM reached 94.12%. This study has succeeded in
developing a model for the detection of depression and anxiety in Twitter
users.
