This repository contains a deep learning model for detecting suicidal tweets using a Twitter dataset. The model is trained to classify tweets as either indicative of suicidal ideation or not.
The dataset used for training and evaluation consists of tweets collected from Twitter. Each tweet in the dataset is labeled as either indicating suicidal thoughts or not.
The deep learning model is built using state-of-the-art natural language processing techniques. It utilizes a neural network architecture, likely employing recurrent neural networks (RNNs), convolutional neural networks (CNNs), or transformer-based models like BERT, to analyze the text content of the tweets and make predictions.
- Data Preprocessing: Before training the model, the dataset needs to be preprocessed. This involves steps such as tokenization, text cleaning, and encoding.
- Model Training: Train the deep learning model using the preprocessed dataset. This step involves feeding the data into the model and adjusting its parameters to minimize classification errors.
- Evaluation: Evaluate the trained model's performance using metrics such as accuracy, precision, recall, and F1-score. This step helps assess how well the model can identify suicidal tweets.
- Deployment: Once the model achieves satisfactory performance, it can be deployed to classify tweets in real-time. This could involve integrating the model into a web application, API, or other platforms.
- Python 3.x
- TensorFlow or PyTorch (depending on the chosen deep learning framework)
- Pandas
- NumPy
- Scikit-learn
- NLTK (Natural Language Toolkit) or SpaCy (for text preprocessing)
- Mayank Raj
- Vaidik Chhirolya
- Shreyas Kumar
- Susan Shilbi
This project is licensed under the MIT License.