brayvid / tweet-sentiment-classifier

Flatiron School Data Science Bootcamp Phase 3 Project

Tweet Sentiment Classifier

Blake Rayvid - https://github.com/brayvid

Flatiron School Data Science Bootcamp Phase 3 Project

Presentation slides

Business problem

Brand reputation management

Monitor brand perception by correctly classifying new tweets as positive, negative or neutral.

• Analyze negative feedback for insights into product weaknesses and use this to drive improvements.
• Identify accounts with consistent positive sentiment and offer to collaborate.
• Time launches of new products during periods of high positive sentiment.

Dataset

https://www.kaggle.com/datasets/yasserh/twitter-tweets-sentiment-dataset

• Three classes: positive, negative, neutral in column called sentiment.
• 27,000 tweets formatted as strings in text column.
• selected_text is an additional column containing the substring of each tweet relevant to classification.

Results

I tried several model types, and a Support Vector Classifier (SVC) applied to ‘selected_text’ yielded the best performance. Test set results are summarized below, with precision and recall scores per class and a confusion matrix. Test accuracy was 83%.

Label	Precision	Recall
negative	83%	77%
neutral	78%	91%
positive	93%	80%

Next steps

• Try Word2Vec semantic embedding instead of frequency-based TF-IDF.
• Investigate dimensionality reduction with UMAP or t-SNE.
• Deploy to a web service to classify new tweets in real time.

This project highlights the importance of sentiment analysis in brand reputation management and provides a foundation for further development and deployment in a real-world setting.