Gain insights into NLP concepts using R, including the tm package, corpora, structured data conversion, and advanced search techniques. Discover quanteda and tidytext for text processing.

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This course will cover concepts in natural language processing (NLP). Developers will find this valuable because of the high demand for NLP skills. This includes understanding natural language when building applications like chatbots, sentiment analysis, search engines, and content recommendations. NLP also provides tools for data analysis, personalization, and content filtering.

In this course, you will learn concepts of NLP, how to use the tm package, the use of corpora, how to convert text to structured data, advanced search techniques, metadata, and how to use quanteda, and tidytext.

With this information, you can expect to command in-demand skills in many text-processing-related fields.

Performing Natural Language Processing with R

library(tidyverse, quietly = TRUE)
library(tidytext, quietly = TRUE)
library(readtext, quietly = TRUE)
library(SnowballC, quietly = TRUE)

stemmed <- readtext(file = "data/mws*txt") %>%
  unnest_tokens(word, text) %>%
  filter(!grepl('[[:digit:]]', word)) %>%
  anti_join(stop_words, by = "word") %>%
  mutate(stem = wordStem(word)) 

print(stemmed, n = 100)


Learn how tidytext uses SnowballC and Hunspell to accomplish stemming.

Before We Begin

Important Concepts in Natural Language Processing

Text Mining Package

Understanding Corpora and Sources

Converting Text to Structured Data

Document Insights and Advanced Search Techniques

Working with Metadata in the tm Package

Implementing NLP with the quanteda Package

Implementing NLP with the tidytext Package

Assess What You Have Learned About NLP

Concluding Remarks

Appendix

Stemming with tidytext

Stemming with SnowballC