This GitHub repository project that aims to extract sentiments from social media data about the Ugandan Ebola outbreak using advanced deep learning techniques. The project utilizes Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and BERT models to analyze the sentiments expressed in the social media posts about the Ebola outbreak in Uganda.

The repository provides a robust framework that includes data collection, preprocessing, model training, and sentiment extraction. It offers a step-by-step guide and code implementation for each stage of the process, ensuring easy reproducibility and extensibility.

Key Features:

1. Data Collection: Detailed instructions and scripts are provided to gather social media data specifically related to the Ugandan Ebola outbreak. This includes collecting relevant posts from popular platforms such as Twitter, Facebook, and online forums.
2. Data Preprocessing: The repository provides comprehensive preprocessing techniques to clean and transform the collected data. This involves removing noise, handling missing values, tokenization, and incorporating domain-specific knowledge to enhance the sentiment extraction process.
3. Model Implementation: The project leverages CNN, LSTM, and BERT models to perform sentiment analysis on the preprocessed social media data. The repository contains well-documented code for model architecture design, training, and evaluation.
4. Sentiment Extraction: The trained models are deployed to extract sentiments from the Ugandan Ebola outbreak social media data. The repository offers efficient algorithms and techniques to classify sentiments as positive, negative, or neutral, providing valuable insights into public sentiment during the outbreak.

With this repository, researchers, data scientists, and developers can contribute to the field of sentiment analysis in public health crises by expanding the model capabilities, exploring different datasets, and experimenting with alternative deep learning architectures. By understanding public sentiment during the Ugandan Ebola outbreak, stakeholders can better address concerns, improve communication strategies, and make informed decisions for effective crisis management.

We welcome contributions, feedback, and collaborations to further enhance the project's capabilities and broaden its impact in understanding and addressing public sentiment during outbreaks. Together, let's utilize deep learning techniques to gain valuable insights from social media data and help mitigate the effects of epidemics.