nlp python scikit learn

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language. In this tutorial, we will explore NLP using Python and the Scikit-Learn library. We'll cover the basics of text processing, feature extraction, and text classification.
Before we begin, make sure you have Python installed on your machine. You can install Scikit-Learn using:
Tokenization is the process of breaking down text into individual words or phrases. Scikit-Learn provides a CountVectorizer class that helps us tokenize and convert text into a bag-of-words representation.
Term Frequency-Inverse Document Frequency (TF-IDF) is a technique that assigns weights to words based on their importance in a document. Scikit-Learn provides a TfidfVectorizer class for this purpose.
Now, let's move on to text classification using Scikit-Learn. We'll use a simple example of classifying movie reviews as positive or negative.
This example demonstrates a basic text classification pipeline using TF-IDF vectorization and a Naive Bayes classifier. You can extend this approach to more complex tasks and use different classifiers based on your requirements.
In this tutorial, we covered the basics of NLP with Python and Scikit-Learn. We explored text processing techniques, including tokenization and TF-IDF vectorization, and implemented a simple text classification example. NLP is a vast field, and this tutorial provides a foundation for further exploration and experimentation.
ChatGPT