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R로 배우는 자연어 처리 입문

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업데이트됨 2024. 5.

R로 Natural Language Processing에서 성공하는 데 필요한 모든 기술과 도구를 한눈에 익히세요.

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RMachine Learning

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15 동영상

47 연습 문제

3,750 XP

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이 기초 과정인 R로 배우는 자연어 처리 입문은 텍스트 분석을 시작하는 데 필요한 도구를 갖추도록 설계되었습니다. 자연어 처리(NLP)는 지난 10년간 눈에 띄는 발전을 이룬 데이터 과학의 지속적으로 성장하는 분야입니다. 이 과정에서는 정규 표현식, 토픽 모델링, 개체명 인식 등 기본 개념을 다루고, 향후 분석을 시작하는 데 바로 활용할 수 있는 충분한 예시를 제공합니다.

선수 조건

Intermediate R Introduction to the Tidyverse

1

True Fundamentals

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

2

Representations of Text

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

Manual calculations

TFIDF Practice

Cosine Similarity

An example of failing at text analysis

Cosine similarity example

3

Applications: Classification and Topic Modeling

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

4

Advanced Techniques

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.

Sentiment analysis

tidytext lexicons

Sentiment scores

Sentiment and emotion

Word embeddings

h2o practice

Additional NLP analysis

Reviewing methods #1

Review methods #2

R로 배우는 자연어 처리 입문

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