Text classification from scratch

Text classification is a fundamental task in natural language processing (NLP) where the goal is to assign predefined categories or labels to textual data. In this tutorial, we'll explore how to perform text classification from scratch using TensorFlow, a powerful open-source machine learning library. By building a text classifier from the ground up, you'll gain a deeper understanding of the underlying concepts and mechanisms involved in NLP tasks.

Prerequisites: Before diving into this tutorial, you should have a basic understanding of Python programming and familiarity with TensorFlow. It's also helpful to have some knowledge of NLP concepts such as tokenization, word embeddings, and neural networks. but this tutorial is self explanatory

Steps:

1. Dataset Preparation:

   • Select a suitable dataset for text classification. This could be a collection of labeled text documents with corresponding categories or labels.

   • Preprocess the dataset by cleaning and tokenizing the text data, and then splitting it into training and testing sets.

2. Text Representation:

   • Convert the text data into a format suitable for machine learning models. This typically involves representing each text document as a numerical vector.

   • Explore different text representation techniques such as bag-of-words, TF-IDF, or word embeddings.

3. Model Definition:

   • Define the architecture of the text classification model. You can start with a simple model architecture such as a feedforward neural network or a convolutional neural network (CNN).

   • Experiment with different layers, activation functions, and hyperparameters to improve the model's performance.

4. Training:

   • Train the text classification model using the training dataset. During training, the model learns to classify text documents into predefined categories or labels based on the input features.

   • Monitor the training process by evaluating performance metrics such as accuracy, loss, and validation accuracy.

5. Evaluation:

   • Evaluate the trained model using the testing dataset to assess its performance on unseen data.

   • Calculate evaluation metrics such as accuracy, precision, recall, and F1-score to measure the model's effectiveness in classifying text documents.

6. Fine-tuning and Optimization:

   • Fine-tune the model and experiment with different optimization techniques to further improve its performance.

   • Explore methods for handling overfitting, such as dropout regularization or early stopping.