This project aims to detect tweets related to disaster situations using machine learning techniques.
Twitter is a popular social media platform where users often share information during emergency situations such as natural disasters, accidents, or crises. This project leverages machine learning algorithms to classify tweets as either disaster-related or non-disaster-related. The goal is to assist in real-time monitoring and response efforts during critical events.
- Natural Language Processing (NLP): Various NLP techniques are employed to process and analyze the textual content of tweets.
- Text Preprocessing: Text data is cleaned and preprocessed by removing stop words, punctuation, stemming/lemmatization etc.
- Feature Engineering: Relevant features, such as word frequency, n-grams, and TF-IDF (Term Frequency-Inverse Document Frequency), are extracted to represent the tweet data.
- Machine Learning Classification: Classification models, such as Naive Bayes, Support Vector Machines (SVM), or Recurrent Neural Networks (RNNs), are trained to predict whether a tweet is related to a disaster or not.
The project utilizes a labeled dataset of tweets, where each tweet is annotated as either disaster or non-disaster. The dataset is split into training and testing sets, and the classification models are trained using the training data. The trained models are then evaluated using the testing data to measure their performance and accuracy.
- Incorporating advanced deep learning models, such as Long Short-Term Memory (LSTM) networks, to improve classification accuracy.
- Exploring ensemble techniques to combine multiple models and boost overall performance.
- Enhancing the system to handle multi-language tweets and adaptability to various disaster scenarios.
Contributions, bug reports, and feedback are welcome. Feel free to open issues or submit pull requests on the GitHub repository.
This project is licensed under the MIT License.