Text Classification with Python | Natural Language Processing Course Part 5| Scikit Learn / sklearn

Text classification is a fundamental task in Natural Language Processing (NLP) that involves categorizing text documents into predefined classes or categories. In this article, we will explore how to perform text classification using Python and the Scikit Learn library.

Introduction to Text Classification

Text classification is a supervised machine learning task that involves training a model to predict the category or class of a given text document. It is commonly used in various applications such as sentiment analysis, spam detection, and topic categorization.

Text Classification with Scikit Learn

Scikit Learn is a popular machine learning library in Python that provides efficient tools for data analysis and modeling. It includes a wide range of algorithms and utilities for various machine learning tasks, including text classification.

To perform text classification with Scikit Learn, we first need to preprocess the text data by converting it into numerical features. This can be done using techniques such as tokenization, vectorization, and feature extraction.

Once the text data is preprocessed, we can train a classification model using algorithms such as Support Vector Machines (SVM), Naive Bayes, or Random Forest. Scikit Learn provides easy-to-use functions for training, evaluating, and testing the classification model.

Conclusion

In this article, we have introduced text classification and demonstrated how to perform it using Python and the Scikit Learn library. Text classification is a powerful technique in NLP that can be applied to various real-world problems. By leveraging the tools and algorithms provided by Scikit Learn, we can build accurate and efficient text classification models.