The Twitter Sentiment Analyzer is a machine learning model designed to classify the sentiment of tweets and messages as positive, negative, or neutral. Initially introduced as a course challenge, I elevated this project by expanding the dataset to 300,000 tweets and enhancing accuracy to over 80%. This model was trained using Create ML and is ready for integration into iOS applications for real-time sentiment analysis.

This project leverages Create ML to train a Core ML sentiment analysis model, moving beyond basic tweet classification. Due to Twitter API’s paid restrictions, this model was adapted to analyze general user-submitted text for sentiment, allowing flexibility without requiring Twitter API access.

• Large-Scale Dataset Handling: Acquired experience training models on large datasets (300,000 tweets) to improve model accuracy.
• Model Optimization and Tuning: Fine-tuned hyperparameters to increase model precision and accuracy significantly.
• Advanced Sentiment Classification: Enhanced understanding of sentiment analysis, applicable to various text sources beyond tweets.

• Dataset preparation and processing for high-volume data
• Sentiment classification and text analysis with Core ML
• Model deployment within iOS apps for real-time user interaction

• Offline Sentiment Analysis: The model operates independently of Twitter API, allowing users to analyze the sentiment of any text input.
• High Accuracy: Achieved over 80% accuracy, making it suitable for practical applications in customer sentiment analysis, feedback processing, etc.

For more information, feel free to reach out:

• Email: [email protected]