Closed zhaoyingjun closed 7 years ago
model.transform()
returns features, not sentiment scores. The sentiment unit discussed in the paper is index 2388 so t = text_features[2388]
may return sensible results. The model has not been tested on pieces of text shorter than phrases however, so I'm not sure what to expect from single words in isolation.
from encoder import Model import matplotlib.pyplot as plt import numpy as np model=Model() text = ['horrendous','good','fuck','nice','well','bad','face','me'] text_features = model.transform(text) for i in range(len(text)): t = np.sum(text_features[i]) print(text[i],t)
but the result: 12.180 seconds to transform 8 examples horrendous -18.1347 good -35.834 fuck -15.2731 nice -26.8903 well -7.1143 bad -17.0147 face -2.13978 me 4.94617