in this code we used libebarys:
- tensorflow
- sklearn
- re
- nltk
preprocessed data was saved at dataF.csv which the punctuation, stopwords, and links have been removed from data and words have stemmed.
7 models have trained with oversampled data(to balance the number of class samples) and the prediction results for validation data was:
| model | accuracy |
|---|---|
| RNN | 97% |
| CNN | 96% |
| sequential NN model | 94% |
| Linear Support Vector Machine | 93% |
| Random Forest Classifier | 90% |
| Multinomial Navy Base | 88% |
| Logistic Regression | 84% |
random forest classification model max depth is equal to 5 and The number of trees in the forest is equal to 200.
and the summary of the sequential model is:
| Layer (type) | Output Shape | Param |
|---|---|---|
| embedding (Embedding) | (None, 100, 16) | 160000 |
| global_average_pooling1d | (None, 16) | 0 |
| dense (Dense) | (None, 24) | 408 |
| dense_1 (Dense) | (None, 1) | 25 |