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obtained results with related metrics. With this implementation, we achieved new levels of accuracy with 86% for the CNN
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architecture and 88% for the LSTM architecture.
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We can couple it to our classification models (Fig. 4), realizing transferlearning and then training the model with the labeled
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data in a supervised manner. The new coupled model can be seen in Figure 5 under word2vec model training. The Table 3 shows the
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obtained results with related metrics. With this implementation, we achieved new levels of accuracy with 86% for the CNN
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Table 6: Results from Pre-trained WE + ML models
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| ML Model | Accuracy | F1 Score | Precision | Recall |
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|:--------:|:---------:|:---------:|:---------:|:---------:|
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obtained results with related metrics. With this implementation, we achieved new levels of accuracy with 86% for the CNN
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architecture and 88% for the LSTM architecture.
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Table 6: Results from Pre-trained WE + ML models
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| ML Model | Accuracy | F1 Score | Precision | Recall |
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|:--------:|:---------:|:---------:|:---------:|:---------:|
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