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.

Simple word clustering

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

Adding to Your TM Skills

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

Amazon vs. Google

Mengorganisasi proyek text mining

Langkah 1: Definisi masalah

Langkah 2: Mengidentifikasi sumber teks

Langkah 3: Pengorganisasian teks

Pengorganisasian teks

Bekerja dengan ulasan Google

Langkah 4 & 5: Ekstraksi fitur & analisis

Ekstraksi fitur & analisis: amzn_pros

Ekstraksi fitur & analisis: amzn_cons

dendrogram amzn_cons

Asosiasi kata

Tinjauan cepat ulasan Google

Pertarungan seru! Ulasan pro Amazon vs. Google

Pertarungan seru, bagian 2! Ulasan negatif

Langkah 6: Tarik kesimpulan

Tarik kesimpulan, wawasan, atau rekomendasi

Tarik kesimpulan, wawasan, atau rekomendasi lain

Selesai!

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

Langkah 1: Definisi masalah

Text Mining dengan Bag-of-Words di R

Latihan interaktif praktis