Comments (2)
GK: Did the initial EDA and feature extraction on home DB.
If we have manual extraction (feature phrase, sentiment) for 20 reviews, we can treat it as ground truth and use it to improve the accuracy of our algorithm
from insightsfromreviews.
The current extracted features are in features.ipynb
from insightsfromreviews.
Related Issues (13)
- Week 4 report
- Verify model output
- Run the model on all reviews in Home category (~500k reviews)
- Test iteration 1 of the wireframe + Model output with 2 users
- Iteration 2 of the wireframe
- First wireframe for input and putput
- Rudimentary EDA to settle on the product category for MVP
- Basic NLP task, extract one feature from a review HOT 1
- Word Clouds HOT 1
- Extract features and arrange by frequency HOT 1
- Find the sentiment for a feature phrase
- Connecting Data Science Pipeline to UI
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from insightsfromreviews.