The document describes the process of text mining using RStudio, authored by Ashley, an SEO specialist in London. It outlines a case study centered on a website redesign involving keyword analysis and competitive landscape evaluation, emphasizing the need for efficient and regular text mining processes. Key tools and packages in R for text mining, including tm, reshape2, dplyr, and stylo, are discussed alongside the essential steps for cleaning and analyzing text data.

1. Text mining with RStudio
| @AshLindley

2. Who am I?
I'm Ashley, I SEO things for a Media Agency in London.

3. Why?
As part of a website redesign and
migration we undertook a large piece of
analysis.
The output was fantastic but it took a lot
of people a very long time.
Great for a one off project, but
unfeasible to run with any kind of
regularity in its current format.
Keyword
Analysis
Social Listening
COMPETITIVE LANDSCAPE
Landscape
Gaps /
Opportunities
STRUCTURE, CONTENT & FORMAT OF NEW SITE
Keyword
Mapping
URL Structure Landing Pages
NEEDS ANALYSIS

4. Why use R?
WHY

5. WHYR?WHY
Recipe:
Packages
tm: A framework for text mining
applications within R
reshape2: Reshape data
dplyr: A grammar of data manipulation
stylo: easy-to-use implementations of
various established analyses in the field
of computational stylistics

6. WHYR?WHY
Recipe:
Functions
First are the functions that will be used
to remove all the rubbish:
• shortened links
• “RT” and “via”
• usernames
• non-alphanumeric
Second is to setup the word/phrase
frequency function to use after the data
has been cleaned.

7. WHYR?WHY
Recipe:
Cleaning
Here we remove numbers,
capitalisation, common words,
punctuation, and otherwise prepare the
text for analysis.

8. WHYR?WHY
Recipe:
ngrams

10. RECIPEWHYR?WHY
What next?
shinyapps.io

11. Feedback? Questions?
| @AshLindley