Comments (3)
2388 is the unit visualized and the single experiment results are reported on in the paper
from generating-reviews-discovering-sentiment.
Referenced in #3 - it's something to do with how HTMLParser.HTMLParser().unescape(text) works. I ran into the same problem trying it with Python 2, but wasn't able to resolve it.
from generating-reviews-discovering-sentiment.
Extracting the 2388-th does not work for me. I always get 0 as value.
I used examples that are predicted as positive in the paper.
Example:
if __name__ == '__main__':
mdl = Model()
text = [
'too bad!',
'it was so cool, beautiful',
'the screenplay and the directing were horrendous',
'best books'
]
text_features = mdl.transform(text)
print('shape')
print(text_features.shape)
sentiment = text_features[:, 2388]
print('sentiment')
print(sentiment)
Result:
0.000 seconds to transform 4 examples
shape
(4, 4096)
sentiment
[ 0. 0. 0. 0.]
Apparently I get 0. for every value of the feature vector extracted, there must be an error somewhere else I guess, looking for it.
from generating-reviews-discovering-sentiment.
Related Issues (20)
- Weight Regularization/hidden state clipping parameters
- Can it work on chinese ? how can I train my chinese text dataset to use this? HOT 6
- local sentiment HOT 1
- what is the range of sentiments? is it -4 to 2? HOT 1
- training speed confirmation HOT 1
- Tips for training on GPU? HOT 2
- Transforming on one VM takes 20 sec VS 120 sec on a different VM
- Transform using multiple GPUs HOT 1
- Question: How long does it take you to run sst_binary_demo.py?
- issue about run sst_binary_demo HOT 1
- Some hint about loading the npy with correlated paramters
- Code compatibility with Tensorflow 1.7
- Visualization ambiguity HOT 4
- Invalid literal HOT 2
- What is the process of extracting features from text?
- How to 'use' this model in our own project ? HOT 14
- How to generate text?
- How to extract vectors? HOT 1
- Opinion2vec? HOT 2
- Punctuation error
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