manish7272/disaster-tweet-sentiment-analysis-a-predictive-approach

Disaster Tweet Sentiment Analysis A Predictive Approach.
🙂😐🙁

🔷Introduction.

This project aims to predict disaster-related tweets through the implementation of Natural Language Processing (NLP) techniques. By leveraging deep learning models and preprocessing methodologies, the project offers a real-time solution for identifying tweets that may be associated with disasters.

🔷Screenshots of Tweet is Disaster or Not.

🌞🌞 -> Non-disaster Tweet.

🚩🚩 -> Tweet is related to a Disaster.

🔷Project Objectives.

• Develop a robust NLP model for disaster tweet analysis.
• Integrate the NLP model into a user-friendly Streamlit application.
• Provide accurate predictions and probabilities for real-time disaster monitoring.

🔷Data Preprocessing with NLP.

The project employs comprehensive NLP-driven data preprocessing steps to ensure meaningful analysis. Key preprocessing steps include:

• Lowercasing, HTML tag removal, and handling special characters.
• Emoji, URL, and Twitter username removal.
• Acronym and contraction substitution using predefined dictionaries.
• Lemmatization, stopwords removal, and custom noise reduction.

🔷NLP Model Development.

The NLP model is designed to effectively classify tweets into disaster and non-disaster categories. Notable aspects of the model development include:

• Tokenization and lemmatization as key NLP techniques.
• High-level architecture overview without disclosing specific model names.
• Training details and considerations for deep learning in NLP.

🔷Streamlit Application Integration.

The NLP model is seamlessly integrated into a Streamlit application, providing a user-friendly interface for real-time tweet analysis. Key features of the Streamlit application include:

• User input processing for dynamic tweet analysis.
• Display of predictions, probabilities, and an engaging user interface.

🔶Learning Outcomes.

This project has provided valuable learning experiences and insights in various aspects, including:

🔸 Natural Language Processing (NLP):

• Data Preprocessing: Gained proficiency in preprocessing textual data for NLP applications, addressing challenges such as emoji handling, acronym substitution, and stopwords removal.
• Tokenization and Lemmatization: Implemented effective tokenization and lemmatization techniques for feature extraction from text data.

🔸 Streamlit Application Development:

• User Interface Design: Created an interactive and user-friendly interface using Streamlit, enhancing user experience in real-time tweet analysis.
• Integration of Models: Successfully integrated the NLP model into the Streamlit application, allowing seamless user interaction.

🔸 Collaboration and Future Development:

• Collaborative Opportunities: Recognized potential avenues for collaboration, including adapting the model for different languages and disaster types.
• Future Enhancements: Identified areas for future development, such as incorporating additional features or data sources for improved prediction.

🔸 Documentation and Communication:

• README Composition: Practiced the creation of a comprehensive README file, effectively communicating the project's objectives, methodologies, and outcomes.
• Closing Statement: Crafted a closing statement expressing gratitude and acknowledging the project's impact, emphasizing the importance of ongoing research in NLP.

These learning outcomes collectively contribute to an enriched understanding of NLP, deep learning, application development, and the real-world implications of predictive models in disaster-related contexts.