In this chapter, you'll learn the basics of using the bag-of-words method for analyzing text data.

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

Jumping into Text Mining with Bag-of-Words

This chapter will teach you how to visualize text data in a way that's both informative and engaging.

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

Word Clouds and More Interesting Visuals

In this chapter, you'll learn more basic text mining techniques based on the bag of words method.

Pengelompokan kata sederhana

Uji pemahaman Anda tentang text mining

Matriks jarak dan dendrogram

Buat TDM yang ramah untuk dendrogram

Gabungkan semuanya: dendrogram berbasis teks

Estetika dendrogram

Menggunakan asosiasi kata

Melebihi Kata Tunggal

Tokenisasi n-gram

Mengubah n-gram

Bagaimana bigram memengaruhi word cloud?

Kriteria frekuensi yang berbeda

Mengubah bobot frekuensi

Menangkap metadata di tm

Adding to Your TM Skills

This chapter ties everything together with a case study in text mining for HR analytics.

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

Finished!

Battle of the Tech Giants for Talent

Coffee tweets

Chardonnay tweets

Anonymous online reviews: Amazon

Anonymous online reviews: Google

Diperkirakan lebih dari 70% informasi bisnis yang berpotensi digunakan bersifat tidak terstruktur, sering kali berupa data teks. Text mining menyediakan kumpulan teknik yang memungkinkan kita memperoleh wawasan yang dapat ditindaklanjuti dari data tidak terstruktur. Dalam kursus ini, kita mempelajari dasar-dasar text mining menggunakan metode bag of words. Tiga bab pertama memperkenalkan berbagai topik penting untuk menganalisis dan memvisualisasikan data teks. Bab terakhir memungkinkan Anda menerapkan semua yang telah dipelajari dalam studi kasus dunia nyata untuk mengekstrak wawasan dari ulasan karyawan di dua perusahaan teknologi besar.

Intermediate R

Pelajari dasar text mining dengan metode bag of words dan topik penting lainnya untuk analisis data teks di R.

Text Mining dengan Bag-of-Words di R

Pelajari teknik bag of words untuk penambangan teks menggunakan R.

Pencarian Teks di R

Tokenisasi n-gram

Text Mining dengan Bag-of-Words di R

Latihan interaktif langsung