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Introduction to Text Analysis in R
4.8(62)27k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
Unstructured text data from customer reviews and social media contains invaluable business intelligence. This intermediate R course demonstrates how to clean, wrangle, visualize, and model text data using modern tidytext principles, sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling.
Who It's For
R users, marketing analysts, and data scientists looking to extract quantitative insights and sentiment trends from unstructured text collections.
Key Takeaways
- Tidy Text Wrangling: Tokenize text strings, eliminate stop words, and calculate term frequencies using tidytext and dplyr.
- Sentiment Analysis: Evaluate customer sentiment by joining text tokens with lexicon dictionaries (AFINN, bing, nrc).
- Topic Modeling: Fit and interpret Latent Dirichlet Allocation (LDA) topic models to discover hidden themes in document corpora.
About this course
From social media to product reviews, text is an increasingly important type of data across applications, including marketing analytics. In many instances, text is replacing other forms of unstructured data due to how inexpensive and current it is. However, to take advantage of everything that text has to offer, you need to know how to think about, clean, summarize, and model text. In this course, you will use the latest tidy tools to quickly and easily get started with text. You will learn how to wrangle and visualize text, perform sentiment analysis, and run and interpret topic models.
Price
Free
This course is free to enrol.
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