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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Sentiment analysis


Opinion mining or sentiment analysis is a hot, new research field dedicated to the automatic evaluation of opinions as expressed on social media, product review websites, or other forums. Often, we want to know whether an opinion is positive, neutral, or negative. This is, of course, a form of classification as seen in the previous section. As such, we can apply any number of classification algorithms. Another approach is to semiautomatically (with some manual editing) compose a list of words with an associated numerical sentiment score (the word "good" can have a score of 5 and the word "bad" a score of -5). If we have such a list, we can look up all words in a text document and, for example, sum up all the found sentiment scores. The number of classes can be more than three, like a five-star rating scheme.

We will apply Naive Bayes classification to the NLTK movie reviews corpus with the goal of classifying movie reviews as either positive or negative. First, we will...

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