Model Definition

After preprocessing our text data, the next step is to define the architecture of our text classification model.

Model Architecture:

• We'll define the structure of our model, including the layers it consists of and how they are connected.

• For text classification, common choices for layers include embedding layers, recurrent layers (like LSTM), and dense layers.

• The embedding layer learns to represent words as dense vectors in a lower-dimensional space.

• Recurrent layers, like LSTM (Long Short-Term Memory), are effective for capturing sequential patterns in text data.

• Dense layers are used for making predictions based on the features learned by the previous layers.

Experimentation:

from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense

embedding_dim = 50  # Adjust based on your preferences

model = Sequential()
model.add(Embedding(input_dim=max_words, output_dim=embedding_dim, input_length=max_len))
model.add(LSTM(100))
model.add(Dense(6, activation='softmax'))


# Compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

1. Embedding Layer:

   • The first layer of the model is an Embedding layer. This layer learns to represent words as dense vectors in a lower-dimensional space.

   • input_dim=max_words: Specifies the size of the vocabulary, i.e., the maximum number of unique words that the model can learn from.

   • output_dim=embedding_dim: Specifies the dimensionality of the embedding vectors. Each word in the vocabulary will be represented by a vector of this size.

   • input_length=max_len: Specifies the length of input sequences. All input sequences will be padded or truncated to this length.

2. LSTM Layer:

   • After the embedding layer, an LSTM (Long Short-Term Memory) layer is added. LSTM is a type of recurrent neural network (RNN) that is effective for capturing sequential patterns in text data.

   • units=100: Specifies the dimensionality of the output space of the LSTM layer, i.e., the number of LSTM units or neurons in the layer.

3. Dense Layer:

   • The final layer of the model is a Dense layer with a softmax activation function.

   • units=6: Specifies the dimensionality of the output space, which is the number of categories or labels in our classification task. In this case, there are 6 categories.

   • activation='softmax': Applies the softmax function to the output, which converts the raw output scores into probabilities. Each output neuron represents the probability of the corresponding category.

4. Compilation:

   • After defining the architecture, the model is compiled using the compile() method.

   • optimizer='adam': Specifies the optimizer used during training. Adam is a popular optimization algorithm known for its efficiency and effectiveness.

   • loss='categorical_crossentropy': Specifies the loss function used to compute the model's error during training. Categorical crossentropy is commonly used for multi-class classification tasks.

   • metrics=['accuracy']: Specifies the evaluation metric used to monitor the model's performance during training. Here, we're monitoring accuracy, which measures the percentage of correctly classified samples.