Text classification Neural Network|Neural Network Text classification Project|Text Classification NN

NLP Python code :

A text classification neural network is a type of artificial neural network designed to analyze and categorize textual data into predefined classes or categories. Text classification is a common task in natural language processing (NLP) and machine learning where the goal is to automatically assign a label or category to a given piece of text.

Here's a breakdown of the key components involved in a text classification neural network:

Input Layer:

Text data is typically represented as a sequence of words or tokens. The input layer of the neural network processes this sequential data.
Embedding Layer:

An embedding layer is often used to convert words or tokens into dense vectors. This layer helps capture semantic relationships between words by placing similar words closer together in the vector space.
Recurrent Layers (optional):

Recurrent layers, such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU), are used to capture the sequential dependencies in the text data. They are particularly useful for understanding the context and relationships between words in a sentence.
Dense Layers:

Dense layers are used for the final classification. The output of the recurrent layers or embedding layer is flattened and connected to one or more dense layers. The number of nodes in the output layer corresponds to the number of classes or categories in the classification task.
Activation Function:

The activation function in the output layer depends on the nature of the classification problem. For binary classification tasks, a sigmoid activation function is commonly used, while softmax is often used for multi-class classification.
Loss Function:

The choice of the loss function depends on the specific classification problem. For binary classification, the binary cross-entropy loss is commonly used, and for multi-class classification, categorical cross-entropy is often employed.
Optimization Algorithm:

Gradient descent-based optimization algorithms, such as Adam or RMSprop, are used to minimize the loss function and update the weights of the neural network during training.
Training:

The model is trained on a labeled dataset, where each input text is associated with a corresponding class label. During training, the model learns to map input texts to the correct output labels.
Evaluation and Prediction:

Once trained, the model is evaluated on a separate test dataset to assess its performance. It can then be used to predict the classes of new, unseen text data.
Text classification neural networks are applied in various real-world applications, including spam detection, sentiment analysis, topic categorization, and many others where the goal is to automatically assign predefined labels or categories to textual data.