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5 days ago Convert all text to lower case.Remove unnecessary symbols and stop words.Tokenize the text (convert sentences into word tokens).Convert the tokens into integer indices.Pad the sequences to have the same length for training.
1. Data Collection: Gather the text data you want to classify.
2.
Data Preprocessing:Convert all text to lower case.Remove unnecessary symbols and stop words.Tokenize the text (convert sentences into word tokens).Convert the tokens into integer indices.Pad the sequences to have the same length for training.
3. Convert all text to lower case.
4. Remove unnecessary symbols and stop words.
5. Tokenize the text (convert sentences into word tokens).
6. Convert the tokens into integer indices.
7. Pad the sequences to have the same length for training.
8.
Model Building:Define an LSTM model architecture using a deep learning library like Keras.Add an embedding layer to convert word indices into dense vectors of fixed size.Add LSTM layers to process the sequence data.Include a dense layer with an activation function (like softmax) for classification.
9. Define an LSTM model architecture using a deep learning library like Keras.
10. Add an embedding layer to convert word indices into dense vectors of fixed size.
11. Add LSTM layers to process the sequence data.
12. Include a dense layer with an activation function (like softmax) for classification.
13.
Model Compilation:Compile the model with a loss function (like categorical_crossentropy for multi-class classification), an optimizer (like Adam), and metrics (like accuracy).
14. Compile the model with a loss function (like categorical_crossentropy for multi-class classification), an optimizer (like Adam), and metrics (like accuracy).
15.
Model Training:Train the model on your preprocessed data.Use a validation set to monitor the model's performance.
16. Train the model on your preprocessed data.
17. Use a validation set to monitor the model's performance.
18.
Evaluation and Tuning:Evaluate the model on a test set.Tune the model parameters or architecture if necessary to improve performance.
19. Evaluate the model on a test set.
20. Tune the model parameters or architecture if necessary to improve performance.
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