PyABSA
glasgow-litter
| PyABSA | glasgow-litter | |
|---|---|---|
| 2 | 2 | |
| 874 | 3 | |
| - | - | |
| 5.1 | 6.1 | |
| 1 day ago | 7 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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PyABSA
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Extracting targeted sentiment from from product reviews
Check out: https://github.com/yangheng95/PyABSA
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[D] How to best extract product benefits/problems from customer reviews using NLP?
https://github.com/yangheng95/PyABSA - for extraction of aspects + corresponding sentiments
glasgow-litter
- Exploring the relationships between deprivation and litter on the streets of Glasgow
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Is this realistic for a undergraduate senior project?
Seems like you should be able to do this considering this project has a great outline for you to follow on a similar project: https://github.com/Garee/glasgow-litter
What are some alternatives?
BERTopic - Leveraging BERT and c-TF-IDF to create easily interpretable topics.
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SelSum - Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.
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