This project analyzes tweets related to the Ukraine-Russia war using Natural Language Processing (NLP) and Machine Learning techniques. The goal is to extract insights, visualize data, and build a model for sentiment classification.
- Scraping Tweets from Twitter
- Data Cleaning: Stopword removal, lemmatization, and preprocessing
- Word Embeddings for text representation
- Sentiment Analysis of tweets
- Data Visualization using various graphs
- Word Cloud for key terms analysis
- Machine Learning Model training
- Feature extraction from NLP pipeline
- Random Forest Algorithm for classification
- K-Fold Cross Validation for model evaluation
- Reading & Writing data from text & CSV files
- Python
- Snscrape (for scraping tweets)
- Pandas, NumPy (for data processing)
- Scikit-learn (for ML models)
- Matplotlib, Seaborn (for visualization)
- WordCloud (for text analysis)
- NLTK / SpaCy (for NLP)
- Sentiment distribution (Positive, Negative, Neutral)
- Word Clouds for frequent terms
- Bar charts & Pie charts for insights
This project is for educational purposes and aims to demonstrate Natural Language Processing (NLP) and Machine Learning techniques for text analysis. It helps in understanding how to process real-world data, apply machine learning models, and visualize insights.
- Clone this repository:
git clone https://github.com/shaheerAlam1/ukarine-russia-war-tweet-analysis-using-NLP.git cd ukraine-russia-war-tweets-analysis