Abstract:
"The main goal of this research was to create a sophisticated Natural Language Processing (NLP)
model that used a Bidirectional Long Short-Term Memory (Bi-LSTM) network architecture and
was especially designed for textual data processing. The methodology comprised gathering a
dataset of Twitter tweets that reflected the moods of people with bipolar illness diagnoses, then
carefully cleaning and pre-processing the data to get it ready for analysis. In order to accommodate
texts of different lengths, the model used strategies like sequence padding and TF-IDF
vectorization for feature extraction. This allowed the model to generalize across a wide range of
linguistic spectrums and demonstrated the potential of machine learning in mental health
diagnostics by making it possible to identify minute linguistic patterns that may be indicative of
bipolar disorder.
"
