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