huspark/fine-grained-sentiment-analysis-with-bert
This project uses BERT(Bidirectional Encoder Representations from Transformers) for Yelp-5 fine-grained sentiment analysis. It also explores various custom loss functions for regression based approaches of fine-grained sentiment analysis.
This project helps customer experience analysts and product managers accurately gauge public opinion. It takes raw customer reviews or feedback text and outputs a precise sentiment score between 0 and 4, indicating fine-grained positivity or negativity. This allows for a more nuanced understanding than a simple positive/negative/neutral classification.
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Use this if you need to deeply understand the sentiment in customer reviews, distinguishing subtle differences in opinion beyond basic positive or negative labels.
Not ideal if you only need a general positive/negative/neutral sentiment classification, or if your computational resources are limited for training.
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Jan 29, 2020
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