Using python for keyword research

1. Answer, why
this
presentation
matters?
Utilizing The Natural
Language Toolkit for
Keyword Classification &
Cluster Analysis
Miracle Inameti-Archibong | Erudite
SLIDESHARE.NET/USERNAME
@mira_inam

2. How to do
keyword
research and
keep the will
to live

3. B R I G T H O N S E O S U M M E R 2 0 2 1
Why Keyword
Research.

4. Keyword research is the
process of identifying
which terms or phrases
your target audience use
to search for your goods,
services.

5. Why do we
need it?

6. Enables you analyse
user behaviour in
order to evaluate if
your content
satisfies the
searcher’s intent.

7. Discover new
Opportunity.
Related products.
Should filtered pages be
optimised and indexable.
How do your consumers'
research.

8. Create a search
and user-
friendly
information
architecture.

9. What do we
need for
keyword
research?

10. Keywords and search
volume
*other keyword Research Tools exist
https://erudite.agency/insights/how-to-conduct-an-in-depth-

11. Analysing the
data.
Categories.
Use case.
Intent.
@mira_inam #BrightonSEO

12. Manual
keyword data
sorting .
Using excel, look for terms or
phrases that occur frequently
and tag them up in the desired
category.

13. Cleaning
Categorisation
Analysis
10,000
Keywords
Clocked hours
28:45:00

14. How to do
keyword
research and
keep the will
to live

15. B R I G H T O N S E O S U M M E R 2 0 2 1
Automating frequency
tagging with the
Natural Language Tool
Kit (NLTK).

16. B R I G H T O N S E O S U M M E R 2 0 2 1
NLTK is a leading
platform for building
Python programs to work
with human language
data.
Source - https://www.nltk.org/

17. Natural language processor with
python
Classification Tokenization Stemming
Tagging Parsing
Semantic
Reasoning

18. Natural language processor with
python
Classification
Tokenization
Pull out various tokens
from text and classify
them by nouns, verbs

19. Natural language processor with
python
Instruction Determine categories
based on word
frequency

20. Everyday applications of NLP
Chat boxes
Voice commands on various devices
Google’s knowledge graph – evolved
from keyword lookup to language parsing

21. B R I G H T O N S E O S U M M E R 2 0 2 1
Getting started.

22. Download and install python library.
Python.org/downloads/
(preferably version 3.6.4)

23. B R I G H T O N S E O S U M M E R 2 0 2 1
You also need the
following python
libraries.

24. Powerful data
structures for data
analysis, time
series, and
statistics.
pandas 1.2.4
https://pypi.org/project/pandas/

25. A simple API for common
natural language
processing tasks such as
part-of-speech tagging,
noun phrase extraction,
sentiment analysis,
classification,
translation, and more.
https://textblob.readthedocs.io/en/dev/
TextBlob

26. Both libraries for reading
data and formatting
information from Excel
files .xls format
https://xlrd.readthedocs.io/en/latest/
https://openpyxl.readthedocs.io/en/stable/
&

27. B R I G H T O N S E O S U M M E R 2 0 2 1
How to pip install the
python libraries

28. Pip install these libraries.
Download requirement(1).txt file and save
it on your computer (note the file path)

29. Open the command prompt.
Open Windows Menu
Type command prompt
❑In the Command Prompt
Copy and paste - pip install -r
C:somepathtorequirement
s.txt
@mira_inam #BrightonSEO

30. Paste the instruction and click enter.
pip install -r
C:somepathtorequi
rementsrequirements
.txt
Highlighted in pink is the path
where your file is saved
@mira_inam #BrightonSEO

31. The analysis script.
Download and save the python script

32. Prepare your keywords list.
❑Ensure your keywords list is
clean, has no spaces or
empty rows
❑Heading must be keywords
❑File must be .xlsx not CSV

33. Running the analysis.
❑Open a command prompt
❑Run the script
⮚ Python C:somepathtokeywords.py
C:somepathtokeywordlist.xlsx
C:somepathtotest_output.xls

34. Replace highlight with where your file is
saved.
C:somepathtokeywords.py-
C:somepathtokeywordlist.xlsx

35. Replace highlight with where you want
your test output file to be saved.
C:somepathtotest_output.xls
@mir_inam #BrightonSEO

36. Run the script.
python C:UsersmiracDownloadskeywords.py
C:UsersmiracDesktoptest.xlsx C:UsersmiracDownloadstest_output.xls

37. Output.

38. Sort the data to identify trends.

39. Spot topics & themes from the data.

40. B R I G H T O N S E O S U M M E R 2 0 2 1
Running the script on
Google Colab

41. Make a copy and save on your
google drive
http://bit.ly/keywordclassification

42. Make a copy and save on your
google drive
Click on the folder
to open upload
data prompt

43. Upload your keywords data.
Upload the file you
want to run.
Same rules about
uploading a clean
file still apply see
slide 31

44. Very important – Keep all naming
conventions the same.
Keywords list – test.xlsx

45. Click run all or Ctrl+F9.
Run all

46. Download the output file.
Right click the 3
dots and
download the
output file

47. Download the output file.
It can be a bit buggy so if you
don’t see your output file wait
a few minutes or re-run.

48. B R I G H T O N S E O S U M M E R 2 0 2 1
Cluster
Classification

49. Pulls out the most common clusters by
frequency in a histogram.
Coffee Dresses

50. Pulls out word clouds.

51. Make a copy and save on your
google drive
http://bit.ly/keywordcluster
How to use.

52. Running the data.
Same way to
upload data and
run as slide 32-35

53. Increase the number of clusters
depending on the size of the data.
Click on cluster
analysis to view
the code.

54. Increase the number of clusters
depending on the size of the data.
Increase or
decrease the
number of clusters

55. No download.
Scroll to the bottom of the script
to use images of the cluster.

56. Feedback.
 What additional functions would you
like to see?
 Send me a message @mira_inam .

57. Thanks to
Eniola Olaleye @galileoeni
Mahdi Mairza

58. View my slides
here in full.
erudite.agency/brighton-seo

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