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Overview This dataset was created for a machine learning project aimed at distinguishing schizophrenia-related content from general discussions in control groups using deep learning models like BiLSTM and BERT.
Dataset Description The dataset consists of Reddit posts collected from schizophrenia-related and control subreddits. After preprocessing, the final dataset comprises:
8,007 posts from r/schizophrenia 9,000 posts each from each of the four control subreddits: 🏋 r/fitness 😂 r/jokes 💑 r/relationships 👨👩👧👦 r/parenting Total: 44,007 posts Split: Training set: 35,205 posts (80%) Validation set: 8,802 posts (20%) Data Format Each post is represented with: input_ids – Tokenized post input for the BERT model. attention_mask – Mask indicating which tokens are real vs. padding. labels – Assigned labels (0 to 4), where: 0: r/schizophrenia 1: r/fitness 2: r/jokes 3: r/relationships 4: r/parenting Usage The dataset can be used for sentiment analysis, mental health research, and NLP classification tasks. The trained models (.pt files) are included for inference.
Model & Training Preprocessing: Tokenization using BERT tokenizer. Models Used: BiLSTM, BERT and hybrid model(BERT+BiLSTM) for classification. Framework: PyTorch.
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