Harnessing NLP for Bipolar Disorder Detection in Textual Data

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. "