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  • Book Overview & Buying Mastering Clojure Data Analysis
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Mastering Clojure Data Analysis

Mastering Clojure Data Analysis

By : Eric Richard Rochester
4.2 (5)
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Mastering Clojure Data Analysis

Mastering Clojure Data Analysis

4.2 (5)
By: Eric Richard Rochester
Buy this Book

Overview of this book

This book consists of a practical, example-oriented approach that aims to help you learn how to use Clojure for data analysis quickly and efficiently. This book is great for those who have experience with Clojure and need to use it to perform data analysis. This book will also be hugely beneficial for readers with basic experience in data analysis and statistics.
Table of Contents (17 chapters)
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Mastering Clojure Data Analysis
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Mastering Clojure Data Analysis
Credits
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Credits
About the Author
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About the Author
About the Reviewers
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About the Reviewers
www.PacktPub.com
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www.PacktPub.com
Preface
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Preface
Lock Free Chapter
1
Network Analysis – The Six Degrees of Kevin Bacon
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Network Analysis – The Six Degrees of Kevin Bacon
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Analyzing social networks
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Getting the data
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Understanding graphs
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Implementing the graphs
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Measuring social network graphs
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Visualizing the graph
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Summary
2
GIS Analysis – Mapping Climate Change
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GIS Analysis – Mapping Climate Change
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Understanding GIS
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Mapping the climate change
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Working with map projections
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Working with ArcGIS
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Summary
3
Topic Modeling – Changing Concerns in the State of the Union Addresses
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Topic Modeling – Changing Concerns in the State of the Union Addresses
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Understanding data in the State of Union addresses
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Understanding topic modeling
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Preparing for visualizations
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Setting up the project
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Getting the data
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Summary
4
Classifying UFO Sightings
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Classifying UFO Sightings
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Getting the data
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Extracting the data
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Dealing with messy data
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Visualizing UFO data
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Description
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Topic modeling descriptions
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Hoaxes
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Summary
5
Benford's Law – Detecting Natural Progressions of Numbers
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Benford's Law – Detecting Natural Progressions of Numbers
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Learning about Benford's Law
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Failing Benford's Law
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Case studies
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Summary
6
Sentiment Analysis – Categorizing Hotel Reviews
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Sentiment Analysis – Categorizing Hotel Reviews
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Understanding sentiment analysis
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Getting hotel review data
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Exploring the data
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Preparing the data
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Cross-validating the results
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Calculating error rates
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Using the Weka machine learning library
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Running the experiment
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Examining the results
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Improving the results
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Summary
7
Null Hypothesis Tests – Analyzing Crime Data
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Null Hypothesis Tests – Analyzing Crime Data
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Introducing confirmatory data analysis
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Understanding null hypothesis testing
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Understanding burglary rates
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Exploring the data
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Conducting the experiment
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Interpreting the results
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Summary
8
A/B Testing – Statistical Experiments for the Web
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A/B Testing – Statistical Experiments for the Web
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Defining A/B testing
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Conducting an A/B test
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Summary
9
Analyzing Social Data Participation
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Analyzing Social Data Participation
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Setting up the project
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Summary
10
Modeling Stock Data
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Modeling Stock Data
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Learning about financial data analysis
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Setting up the basics
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Getting prepared with data
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Analyzing the text
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Inspecting the stock prices
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Merging text and stock features
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Analyzing both text and stock features together with neural nets
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Predicting the future
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Taking it with a grain of salt
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Summary
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Index
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Index

Exploring the data

If we look at some of these reviews, we can see just how difficult categorizing the reviews as positive or negative is, even for humans.

For instance, some words are used in ways that aren't associated with their straightforward meaning. For example, look at the use of the term greatest in the following quote from a review for a Beijing hotel:

"Not the greatest area but no problems, even at 3:00 AM."

Also, many reviews recount both good and bad aspects of the hotel that they're discussing, even if the final review decidedly comes down one way or the other. This review of a London hotel starts off listing the positives, but then it pivots:

"… These are the only real positives. Everything else was either average or below average...."

Another reason why reviews are difficult to classify is that many reviews just don't wholeheartedly endorse whatever it is they're reviewing. Instead, the review will be tepid, or the reviewers qualify their conclusions as they did in this review...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Mastering Clojure Data Analysis
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