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Text Mining with Bag-of-Words in R

Intermedio

Updated 12/2024

Learn the bag of words technique for text mining with R.

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RMachine Learning4 horas15 vídeos69 ejercicios5,700 XP43,128Declaración de cumplimiento

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Descripción del curso

It is estimated that over 70% of potentially usable business information is unstructured, often in the form of text data. Text mining provides a collection of techniques that allows us to derive actionable insights from unstructured data. In this course, we explore the basics of text mining using the bag of words method. The first three chapters introduce a variety of essential topics for analyzing and visualizing text data. The final chapter allows you to apply everything you've learned in a real-world case study to extract insights from employee reviews of two major tech companies.

Prerrequisitos

1

Jumping into Text Mining with Bag-of-Words

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What is text mining?

Understanding text mining

Quick taste of text mining

Getting started

Load some text

Make the vector a VCorpus object (1)

Make the vector a VCorpus object (2)

Make a VCorpus from a data frame

Cleaning and preprocessing text

Common cleaning functions from tm

Cleaning with qdap

All about stop words

Intro to word stemming and stem completion

Word stemming and stem completion on a sentence

Apply preprocessing steps to a corpus

The TDM & DTM

Understanding TDM and DTM

Make a document-term matrix

Make a term-document matrix

2

Word Clouds and More Interesting Visuals

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Common text mining visuals

Test your understanding of text mining

Frequent terms with tm

Frequent terms with qdap

Intro to word clouds

A simple word cloud

Stop words and word clouds

Plot the better word cloud

Improve word cloud colors

Use prebuilt color palettes

Other word clouds and word networks

Find common words

Visualize common words

Visualize dissimilar words

Polarized tag cloud

Visualize word networks

Teaser: simple word clustering

3

Adding to Your TM Skills

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Simple word clustering

Test your understanding of text mining

Distance matrix and dendrogram

Make a dendrogram friendly TDM

Put it all together: a text-based dendrogram

Dendrogram aesthetics

Using word association

Getting past single words

N-gram tokenization

Changing n-grams

How do bigrams affect word clouds?

Different frequency criteria

Changing frequency weights

Capturing metadata in tm

4

Battle of the Tech Giants for Talent

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Amazon vs. Google

Organizing a text mining project

Step 1: Problem definition

Step 2: Identifying the text sources

Step 3: Text organization

Text organization

Working with Google reviews

Steps 4 & 5: Feature extraction & analysis

Feature extraction & analysis: amzn_pros

Feature extraction & analysis: amzn_cons

amzn_cons dendrogram

Word association

Quick review of Google reviews

Cage match! Amazon vs. Google pro reviews

Cage match, part 2! Negative reviews

Step 6: Reach a conclusion

Draw conclusions, insights, or recommendations

Draw another conclusion, insight, or recommendation

Text Mining with Bag-of-Words in R

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