Developed a comprehensive NLP project focused on extracting actionable business insights from Amazon reviews of video games. Initially, the sentiment of each review was analyzed to identify negative feedback, utilizing a BERT based model. Following this, an embedding model was applied to transform the reviews into embeddings, facilitating the nuanced understanding of customer opinions beyond mere positive or negative sentiment. Leveraging UMAP for dimensionality reduction and HDBSCAN for clustering, the project effectively grouped reviews into distinct clusters, enabling a focused analysis on specific aspects of customer dissatisfaction.
| Component | Link |
|---|---|
| Upload Raw data to HuggingFace | data_prep_notebooks/upload_dataset_to_gcs.ipynb |
| Upload data from HugginFace to GCS | upload_dataset_to_hugging_face.ipynb |
| Sentiment Analysis | 01_sentiment_analysis.ipynb |
| Clusters | 02_clusters.ipynb |
CREATE OR REPLACE TABLE `amazon_reviews.modeling_data` AS
SELECT
TRIM(r.reviewText, " ") as review,
TRIM(m.brand, " ") as brand,
m.category.list[OFFSET(0)].item AS first_category_item
FROM
`valid-dragon-397303.amazon_reviews.reviews` r
INNER JOIN
`valid-dragon-397303.amazon_reviews.metadata` m
ON
r.asin = m.asin
WHERE
TRIM(r.reviewText, " ") IS NOT NULL AND
TRIM(m.brand, " ") IS NOT NULL AND
ARRAY_LENGTH(m.category.list) > 0
;
