Text Analytics and Predictions with R Essential Training

Text Analytics and Predictions with R Essential Training
Text Analytics and Predictions with R Essential Training
English | MP4 | AVC 1280Ă—720 | AAC 48KHz 2ch | 0h 40m | 132 MB

Social media, emails, blogs, and text messages offer businesses valuable insights into how their customers think and what they want. But mining this text data isn’t a straightforward process; rather, it requires a special set of tools and techniques. In this course, Kumaran Ponnambalam explores these tools and techniques, demonstrating how to use them to analyze text data in R and perform machine learning and predictions. Kumaran shows how to perform text analytics using popular methods like word cloud and sentiment analysis. He then shows how to make predictions with text data using clustering, classification, and recommendations—otherwise known as predictive text.

Topics include:

  • Creating a word cloud
  • Analyzing sentiment
  • Extracting emotions from text
  • Clustering similar entities based on text
  • Using classification for supervised learning
  • Recommending items to users based on text data analytics
Table of Contents

Introduction
1 The need for text analytics
2 Introduction to text analytics
3 Pre-requisites for the course

Word Cloud
4 Word cloud concepts
5 Preparing data
6 Displaying the word cloud
7 Enhancing the word cloud

Sentiment Analysis
8 Sentiment analysis concepts
9 Finding sentiment
10 Summarizing sentiment
11 Analyzing emotions

Clustering
12 Clustering concepts
13 Preparing data for clustering
14 Clustering hashtags
15 Finding optimal cluster size

Classification
16 Classification concepts
17 Preparing data
18 Building a model
19 Running predictions

Predictive Text
20 Predictive text concepts
21 Preparing data
22 Building the n-grams database
23 Predicting text

Conclusion
24 Next steps