Since text is unstructured data, a certain amount of wrangling is required to get it into a form where you can analyze it. In this chapter, you will learn how to add structure to text by tokenizing, cleaning, and treating text as categorical data.

Text as data

Airline tweets data

Grouped summaries

Counting categorical data

Counting user types

Summarizing user types

Tokenizing and cleaning

Tokenizing and counting

Cleaning and counting

Wrangling Text

While counts are nice, visualizations are better. In this chapter, you will learn how to apply what you know from ggplot2 to tidy text data.

Memplot jumlah kata

Memvisualisasikan keluhan

Memvisualisasikan non-keluhan

Meningkatkan plot jumlah kata

Menambahkan stop words kustom

Memvisualisasikan jumlah kata menggunakan faktor

Memfacet plot jumlah kata

Menghitung menurut produk dan menyusun ulang urutan

Memvisualisasikan jumlah kata dengan facet

Memplot word cloud

Membuat word cloud

Tambahkan sentuhan warna

Visualizing Text

While word counts and visualizations suggest something about the content, we can do more. In this chapter, we move beyond word counts alone to analyze the sentiment or emotional valence of text.

Sentiment dictionaries

Counting the NRC sentiments

Visualizing the NRC sentiments

Appending dictionaries

Counting sentiment

Visualizing sentiment

Improving sentiment analysis

Practicing reshaping data

Practicing with grouped summaries

Visualizing sentiment by complaint type

Sentiment Analysis

In this final chapter, we move beyond word counts to uncover the underlying topics in a collection of documents. We will use a standard topic model known as latent Dirichlet allocation.

Latent Dirichlet allocation

Topics as word probabilities

Summarizing topics

Visualizing topics

Document term matrices

Creating a DTM

Evaluating a DTM as a matrix

Running topic models

Fitting an LDA

Tidying LDA output

Comparing LDA output

Interpreting topics

Naming three topics

Naming four topics

Wrap-up

Topic Modeling

Airline tweets

Roomba reviews

Dari media sosial hingga ulasan produk, teks semakin menjadi jenis data yang penting di berbagai aplikasi, termasuk analitik pemasaran. Dalam banyak kasus, teks menggantikan bentuk data tak terstruktur lainnya karena biayanya yang rendah dan sifatnya yang mutakhir. Namun, untuk memanfaatkan seluruh potensi teks, Anda perlu mengetahui cara memikirkan, membersihkan, meringkas, dan memodelkan teks. Dalam kursus ini, Anda akan menggunakan alat tidy terbaru untuk memulai analisis teks dengan cepat dan mudah. Anda akan mempelajari cara merapikan dan memvisualisasikan teks, melakukan analisis sentimen, serta menjalankan dan menafsirkan model topik.

Introduction to the Tidyverse

Pelajari analisis data teks dengan framework tidy di R: analisis sentimen, pemodelan topik, visualisasi.

Memfacet plot jumlah kata

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