Comments (5)
hi @dyngts
Given the review and term (a sequence of words existing in the review), the model will return the sentiment. (is this what you mean by aspect?).
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hi @vanzytay,
Do you mean the model will return overall sentiment polarity for specific review? i thought at first, it will return the aspects (what i mean by aspect is like food, service, ambience in restaurant aspect) with its polarity instead only overall sentiment.
What do you think about that?
Thank you
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No, we only return sentiments and assume the aspect is given.
This is in concert with works like https://aclweb.org/anthology/D16-1058 which doesn't consider the extraction of aspects and assume they are given.
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Hi @vanzytay,
Thanks for the clarification. My concern is the model cannot be used for production when there's always new reviews that don't have any pre-defined annotate review.
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You can always use an off-the-shelf entity extractor for the aspect terms. :)
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Related Issues (8)
- The test loss does not change
- Accuracy unchanged while training HOT 3
- Error using Anaconda Python 3.6 in pickle
- RuntimeError: module compiled against API version 0xb but this version of numpy is 0xa
- modulenorm = p.grad.data.norm() AttributeError: 'NoneType' object has no attribute 'data' HOT 2
- ATT-RNN RuntimeError: invalid argument 0: Tensors must have same number of dimensions: got 2 and 3 at /opt/conda/conda-bld/pytorch_1518238409320/work/torch/lib/THC/generic/THCTensorMath.cu:102
- TypeError: iteration over a 0-d tensor HOT 2
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