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R Data Analysis Projects

You're reading from   R Data Analysis Projects Build end to end analytics systems to get deeper insights from your data

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788621878
Length 366 pages
Edition 1st Edition
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Author (1):
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Gopi Subramanian Gopi Subramanian
Author Profile Icon Gopi Subramanian
Gopi Subramanian
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Table of Contents (9) Chapters Close

Preface 1. Association Rule Mining FREE CHAPTER 2. Fuzzy Logic Induced Content-Based Recommendation 3. Collaborative Filtering 4. Taming Time Series Data Using Deep Neural Networks 5. Twitter Text Sentiment Classification Using Kernel Density Estimates 6. Record Linkage - Stochastic and Machine Learning Approaches 7. Streaming Data Clustering Analysis in R 8. Analyze and Understand Networks Using R

Twitter Text Sentiment Classification Using Kernel Density Estimates

Twitter and other social media applications are generating a lot of unstructured text today at a great velocity.  Today's product companies look at these social media applications as a great source to give them an understanding of how consumers feel about their products or services. Hence, the analysis of these unstructured texts has become very important for these companies.

Text mining as field is gaining importance day by day.  The invention of techniques such as bag-of-words, word2vec embedding has made text data suitable for processing by most machine learning algorithms. Analyzing the sentiment in a text is an active field of research.

Sentiment analysis, also called opinion mining, is the field of study that analyzes people's opinions, sentiments, evaluations, appraisals, attitudes...
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