Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

Analyzing the sentiment of a sentence

Sentiment analysis is one of the most popular applications of NLP. Sentiment analysis refers to the process of determining whether a given piece of text is positive or negative. In some variations, we consider "neutral" as a third option. This technique is commonly used to discover how people feel about a particular topic. This is used to analyze sentiments of users in various forms, such as marketing campaigns, social media, e-commerce customers, and so on.

How to do it…

  1. Create a new Python file, and import the following packages:

    import nltk.classify.util
    from nltk.classify import NaiveBayesClassifier
    from nltk.corpus import movie_reviews
  2. Define a function to extract features:

    def extract_features(word_list):
        return dict([(word, True) for word in word_list])
  3. We need training data for this, so we will use movie reviews in NLTK:

    if __name__=='__main__':
        # Load positive and negative reviews  
        positive_fileids = movie_reviews.fileids('pos')