Your question is Sentiment Tagging from Feedback. Start with the requirements and the two tables on the right.
Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.
Describe how you would use Python or R to clean an unstructured dataset of user feedback and build a baseline sentiment tagger.
For this SQL implementation, clean the feedback text and assign a rule-based baseline sentiment label using the available feedback and channel data. Treat missing or empty feedback as unknown, and preserve feedback even when its channel is unmatched.
feedback_id, channel_name, cleaned_feedback, and sentiment_tagfeedback_id ascending| Column | Type | Description |
|---|---|---|
| feedback_idPK | INT | Unique feedback record identifier |
| channel_id | INT | Identifier of the feedback channel |
| feedback_text | TEXT | Raw unstructured user feedback |
| submitted_at | TIMESTAMP | Timestamp when the feedback was submitted |
| Column | Type | Description |
|---|---|---|
| channel_idPK | INT | Unique channel identifier |
| channel_name | VARCHAR(100) | Human-readable feedback channel name |
| channel_type | VARCHAR(50) | General type of feedback channel |