DAMO-NLP-SG/BGCA
[ACL 2023] Code and Data for "Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis"
This project helps sentiment analysis researchers develop models that can accurately identify opinions on specific aspects of products or services, even when working with data from new domains. It takes raw text reviews or feedback and outputs extracted sentiment elements (aspects, opinions, and their sentiment). Researchers and data scientists focused on natural language processing and sentiment analysis would use this.
No commits in the last 6 months.
Use this if you need to train robust sentiment analysis models that can generalize well across different data sources or product categories, especially when labeled data is scarce in new domains.
Not ideal if you are looking for a pre-trained, ready-to-use sentiment analysis tool for immediate application without model training or fine-tuning.
Stars
39
Forks
4
Language
Python
License
MIT
Category
Last pushed
Aug 02, 2023
Commits (30d)
0
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