| # Sentiment Analysis with GUI | |
| A Deep Learning Model which used for Sentiment analysis. The Accuracy it reach upto 85%. It train on 25000 text data. | |
| # Neural Network Info | |
| The bert layer is integrated in the neural network at the second layer after input layer. The 3 GRU layer is for feature extraction | |
| then a Conv1D Layer is use after that making the output flatten and passing through a bunch of dense layer. | |
| ## Info | |
| 1) "Bert_uncased_model_Tiwtter.h5" has reached to the accuracy upto 85% just on 30 epochs. Loss it got is 0.51. This model is purly train in Twitter dataset. | |
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| 2) "Bert_uncased_model_Reddit.h5" has reached to the accuracy upto 84% just on 35 epochs. Loss it got is 0.81. This model is not purly train in Twitter dataset but a bit of reddit's dataset is also used. | |
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| Loss :- Sparse_categorical_crossentropy | |
| Activatiion on last layer :- softmax | |
| Note: Max input length is 768 words. | |
| ## Screenshots | |
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| ## Libray Used | |
| ##### > opencv | |
| ##### > tensorflow | |
| ##### > numpy | |
| ##### > pickle | |
| ##### > bert | |
| ##### > tensorflow_hub | |
| ##### > tensorflow_text | |
| ## Authors | |
| - [@Somnath Dash](https://www.github.com/somnathdashs) | |