How to Perform Text Classification in Python using Tensorflow 2 and Keras

How to Perform Text Classification in Python using Tensorflow 2 and Keras

In this tutorial, we will build a text classifier model using RNNs using Tensorflow and Keras in Python. Building deep learning models (using embedding and recurrent layers) for different text classification problems such as sentiment analysis or 20 news group classification using Tensorflow and Keras in Python

Building deep learning models (using embedding and recurrent layers) for different text classification problems such as sentiment analysis or 20 news group classification using Tensorflow and Keras in Python

Text classification is one of the important and common tasks in supervised machine learning. It is about assigning a category (a class) to documents, articles, books, reviews, tweets or anything that involves text. It is a core task in natural language processing.

Many applications appeared to use text classification as the main task, examples include  spam filtering,  sentiment analysis, speech tagging, language detection, and many more.

In this tutorial, we will build a text classifier model using RNNs using Tensorflow in Python, we will be using IMDB reviews dataset which has 50K real world movie reviews along with their sentiment (positive or negative). In the end of this tutorial, I will show you how you can integrate your own dataset so you can train the model on it.

tensorflow python keras

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