New answers tagged sentiment-analysis
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How does BERT work for Aspect-Based sentiment analysis?
So, from what I have understood there are two ways to perform ABSA:
Aspect category detection + Aspect category sentiment classification
Aspect target extraction + Aspect target sentiment ...
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unbalanced data on train set and test set
It is often useful to balance a training dataset. For example, if the model learns a decision boundary, that decision boundary will then learn to separate different categories based more on features ...
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unbalanced data on train set and test set
It's also possible to decrease the learning step when updating weights learned from the majority class, and/or increase the learning step when updating weights learned from the minority class.
See ...
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unbalanced data on train set and test set
If your dataset is sufficiently large and you might want to reduce its size for performance reasons anyways, you could do undersampling of label 1.
However, if you only have a limited amount of data ...
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Sentiment analysis BERT vs Model from scratch
In general, BERT is a much stronger model. Word embeddings only represent isolated words, whereas BERT considers the sentence context and how the words interact. With user-generated data, word ...
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