AdilHayat173/disaster_Tweet

Disaster Tweet Classification Model

Description

This project involves developing a machine learning model to classify tweets as indicating a disaster or not. Utilizing Deep Learning techniques, specifically a fine-tuned model from the Hugging Face library, the system is trained on the disaster tweet dataset from Kaggle. The goal is to predict whether a given tweet refers to a disaster event based on its content.

By analyzing critical components of tweets, such as content and context, the BERT model leverages its deep understanding of language to accurately classify whether a tweet indicates a disaster. The model is trained on a comprehensive dataset of disaster-related tweets, enabling it to effectively differentiate between disaster and non-disaster tweets across various contexts.

This classification system can be utilized by emergency responders, news organizations, and social media analysts to quickly identify and respond to disaster-related events or to monitor trends in disaster-related communications.

Technologies Used

Dataset

• Source: Kaggle Disaster Tweets Dataset
• Purpose: Contains tweets labeled to indicate whether they refer to a disaster.

Model

• Base Model: BERT (bert-base-uncased)
• Library: Hugging Face transformers
• Task: Binary text classification

Approach

1. Preprocessing:

   • Load and preprocess the disaster tweet dataset.
   • Tokenize the tweet texts.

2. Fine-Tuning:

   • Fine-tune the BERT model on the preprocessed disaster tweet dataset.

3. Training:

   • Train the model to distinguish between disaster and non-disaster tweets.

Key Technologies

• Deep Learning (BERT): For advanced text classification and contextual understanding.
• Natural Language Processing (NLP): For text preprocessing and analysis.
• Machine Learning Algorithms: For model training and prediction tasks.

Google Colab Notebook

You can view and run the Google Colab notebook for this project here.

Acknowledgements

• Hugging Face for transformer models.
• NLTK for natural language processing.
• Streamlit for creating the interactive web interface.

Author

@AdilHayat

Feedback

If you have any feedback, please reach out to us at hayatadil300@gmail.com.