https://github.com/bluecube246/nlp-assignment-aml-4
Predicting quality of wine using review text and other information.
https://github.com/bluecube246/nlp-assignment-aml-4
classification machine-learning nlp
Last synced: over 1 year ago
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Predicting quality of wine using review text and other information.
- Host: GitHub
- URL: https://github.com/bluecube246/nlp-assignment-aml-4
- Owner: bluecube246
- Created: 2019-10-16T21:48:46.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-02-28T00:23:42.000Z (over 5 years ago)
- Last Synced: 2025-01-28T12:44:26.656Z (over 1 year ago)
- Topics: classification, machine-learning, nlp
- Language: Jupyter Notebook
- Homepage:
- Size: 55.7 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# NLP Assignment AML-4
## The objective of this assignment is to predict wine quality from review text and other qualities of wine.
## Data source: https://www.kaggle.com/zynicide/wine-reviews
### Task 1:
#### 1.1 Create a baseline model for predicting wine quality using only non-text features.
#### 1.2 Create a simple text-based model using a bag-of-words approach and a linear model.
#### 1.3 Try using n-grams, characters, tf-idf rescaling and possibly other ways to tune the BoW model. Be aware that you might need to adjust the (regularization of the) linear model for different feature sets.
#### 1.4 Combine the non-text features and the text features. How does adding those features improve upon just using bag-of-words?
### Task 2:
#### Use a pretrained word-embedding (word2vec, glove or fasttext) for featurization instead of the bag-of-words model. Does this improve classification? How about combining the embedded words with the BoW model?