Chapter 1 of Introduction to Natural Langauge Processing prepares you for running your first analysis on text. You will explore regular expressions and tokenization, two of the most common components of most analysis tasks. With regular expressions, you can search for any pattern you can think of, and with tokenization, you can prepare and clean text for more sophisticated analysis. This chapter is necessary for tackling the techniques we will learn in the remaining chapters of this course.

Regular expression basics

Practicing syntax with grep

Exploring regular expression functions.

Tokenization

tidytext functions

Tokenization: sentences

Text cleaning basics

Text preprocessing: remove stop words

Text preprocessing: Stemming

True Fundamentals

In this chapter, you will learn the most common and studied ways to analyze text. You will look at creating a text corpus, expanding a bag-of-words representation into a TFIDF matrix, and use cosine-similarity metrics to determine how similar two pieces of text are to each other.  You build on your foundations for practicing NLP before you dive into applications of NLP in chapters 3 and  4. 

Understanding an R corpus

Explore an R corpus

Creating a tibble from a corpus

Creating a corpus

The bag-of-words representation

Practice BoW

BoW Example

Sparse matrices

The TFIDF

Manual calculations

TFIDF Practice

Cosine Similarity

An example of failing at text analysis

Cosine similarity example

Representations of Text

Chapter 3 focuses on two common text analysis approaches, classification modeling, and topic modeling. If you are working on text analysis projects, you will inevitably use one or both of these methods. This chapter teaches you how to perform both techniques and provides insight into how to approach these techniques from a practical point of you.

Preparing text for modeling

Data preparation

Removing sparse terms

Classification modeling

Classification modeling example

Confusion matrices

TFIDF tibble vs dtm

Introduction to topic modeling

LDA practice

Assigning topics to documents

LDA in practice

Testing perplexity

Reviewing LDA results

Applications: Classification and Topic Modeling

In chapter 4 we cover two staples of natural language processing, sentiment analysis, and word embeddings. These are two analysis techniques that are a must for anyone learning the fundamentals of text analysis. Furthermore, you will briefly learn about BERT, part-of-speech tagging, and named entity recognition. Almost 15 different analysis techniques were covered in this course, so chapter 4 ends by recapping all of the great techniques you will learn about in this course. 

Analisis sentimen

leksikon tidytext

Skor sentimen

Sentimen dan emosi

Word embeddings

Latihan h2o

word2vec

Analisis NLP tambahan

Meninjau metode #1

Tinjau metode #2

Kesimpulan

Advanced Techniques

Animal Farm

Russian Troll tweets

Seperti halnya kursus fundamental lainnya, Pengantar Natural Language Processing di R dirancang untuk membekali Anda dengan alat yang diperlukan untuk memulai analisis teks. Natural language processing (NLP) adalah bidang yang terus berkembang dalam data science, dengan banyak kemajuan menarik selama dekade terakhir. Kursus ini akan membahas dasar-dasar topik tersebut dan mempersiapkan Anda untuk memperluas kemampuan analisis. Kita akan mendalami regular expression, topic modeling, named entity recognition, dan lainnya, sambil menyajikan contoh menyeluruh yang dapat Anda gunakan untuk memulai analisis selanjutnya.

Intermediate R

Introduction to the Tidyverse

Pelajari dasar NLP di R: ekspresi reguler, pemodelan topik, pengenalan entitas, dan lainnya.

Pengantar Natural Language Processing di R

Dapatkan gambaran menyeluruh tentang semua keterampilan dan alat yang diperlukan untuk menguasai Pemrosesan Bahasa Alami (Natural Language Processing) dalam R.

Analisis NLP tambahan

Create Your Free Account