Adding multi class classification and tuning for dense and dropout la… #208
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…yers including bert model Signed-off-by: nsriharshavardhan <[email protected]>
Signed-off-by: nsriharshavardhan <[email protected]>
The last dataset being used did not represent the data or the sentiments that are precisely required in applying the sentiment analysis yielding lower accuracy. This new BERT model implemented using pytorch rectifies this problem and increases the accuracy to 78%. Signed-off-by: nsriharshavardhan <[email protected]>
Signed-off-by: nsriharshavardhan <[email protected]>
… files, cleaned and documented Signed-off-by: nsriharshavardhan <[email protected]>
Signed-off-by: nsriharshavardhan <[email protected]>
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Changed the sentiments from binary bad, good to multiclass bad, good and neutral adjusted the bidirectional LSTM model accordingly. Added hyperparameter tuning to the Dense and Dropout layers of the model and added couple of extra layers. Also tried BERT model at the end but the accuracy was not satisfactory and the model takes a long time to run.