In this talk, Dido will present practical ways for the creation of a successful content marketing strategy in the cycle of phases of analysis and clustering to implementation, mapping and performance reporting, using a helpful combination of scripts custom programmed in Python. Links to the scripts: https://pypi.org/project/polyfuzz/ https://query-analysis.herokuapp.com/ https://github.com/searchsolved/search-solved-public-seo/blob/main/search_engine_journal/SEJ_Semantic_Clustering_Tool_by_LeeFootSEO.ipynb

1. Python For SEO specialists and
Content Marketing - Hand in Hand
Dido Grigorov | Serpact Ltd.
slideshare.net/DilyanGrigorov
@didogrigorov

2. Agenda
What is Python
?
Why Python
?
Python & Googl
e
Python & SEO?
!
Content marketing For SEO hand-in-
hand with Python

3. What is…

4. Python is an interpreted,
object-oriented, high-level
programming language with
dynamic semantics. Its high-
level built in data
structures, combined with
dynamic typing and dynamic
binding…

5. Python is an interpreted,
object-oriented, high-level
programming language with
dynamic semantics. Its high-
level built in data
structures, combined with
dynamic typing and dynamic
binding…
make it very attractive for
Rapid Application Development,
as well as for use as a
scripting or glue language to
connect existing components
together.

6. Python is a great tool
when you need to
automate tasks

7. It’s the most used
language in th
e
Data scienc
e
Machine learnin
g
Artificial Intelligence
worlds

8. Is great for automation
opportunities for low-level
tasks in SEO which we would
usually spend several hours
undertaking

9. It’s one of the best
for Data Analysis
& Visualization

10. Google & Python
Google BER
T
Google MU
M
Neural Matchin
g
RankBrain

13. RankBrain helps us find
information we weren’t able to
before by more broadly
understanding how words in a
search relate to real-world
concepts.

15. Neural Matching
Neural matching helps us understand fuzzier
representations of concepts in queries and
pages, and match them to one another. It looks
at an entire query or page rather than just
keywords, developing a better understanding of
the underlying concepts represented in them.

16. Python For Content in SEO
Intent classificatio
n
Keywords Synonyms & Similarities
Calculatio
n
Semantic Keywords Clusterizatio
n
Language Model Retrieval by Query

17. Intent classification & Entities
Import librarie
s

18. Intent classification & Entities
Import Queries and Setup Intent List
s

19. What Is Intent
?
Why Is It Important?

20. Informational

21. Navigational

22. Transactional

23. Intent classification & Entities
Import Queries and Setup Intent List
s

24. Intent classification & Entities
Organize Queries into Intent List
s

25. Intent classification & Entities
Display Intent Stats

26. Intent classification & Entities
Entity Recognition

28. Intent classification & Entities
Clean Query Word List

29. Intent classification & Entities
Build Knowledge Graph API

30. Intent classification & Entities
Process Word List Through GK
G

31. Intent classification & Entities
Entity Stat
s

32. Intent classification & Entities
Result

33. Intent classification & Entities
Result

34. Keyword synonyms and similarities
What is PolyFuzz?
PolyFuzz performs fuzzy string matching, string grouping, and
contains extensive evaluation functions. PolyFuzz is meant to bring
fuzzy string matching techniques together within a single
framework.
Currently, methods include a variety of edit distance measures, a
character-based n-gram TF-IDF, word embedding techniques such
as FastText and GloVe, and Transformers embeddings.

36. Keyword synonyms and similarities
Install Polyfuz
z
pip install polyfuz
z
pip install polyfuzz[fast
]
pip install polyfuzz[flair
]
pip install polyfuzz[all
]

37. Keyword synonyms and similarities
How does it work
?
from polyfuzz import PolyFuz
z
from_list = ["apple", "apples", "appl", "recal", "house",
"similarity"
]
to_list = ["apple", "apples", "mouse"
]
model = PolyFuzz("TF-IDF"
)
model.match(from_list, to_list
)

38. Keyword synonyms and similarities
Result…
>>> model.get_matches(
)
From To Similarit
y
0 apple apple 1.000000
1 apples apples 1.000000
2 appl apple 0.783751
3 recal None 0.000000
4 house mouse 0.587927
5 similarity None 0.000000

39. Result…
>>> model.get_matches(
)
From To Similarit
y
0 apple apple 1.000000
1 apples apples 1.000000
2 appl apple 0.783751
3 recal None 0.000000
4 house mouse 0.587927
5 similarity None 0.000000

40. Keyword synonyms and similarities
You can also use Google BERT…

41. Keyword synonyms and similarities
You Can Use Your Own Custom Models …
class MyModel(BaseMatcher)
:
def match(self, from_list, to_list)
:
# Calculate distances
matches = [[fuzz.ratio(from_string, to_string) / 100 for
to_string in to_list]
for from_string in from_list
]
# Get best matches
mappings = [to_list[index] for index in
np.argmax(matches, axis=1)
]
scores = np.max(matches, axis=1
)
# Prepare dataframe
matches = pd.DataFrame({'From': from_list,'To':
mappings, 'Similarity': scores}
)
return matche
s

42. Semantic Keywords Clusterization
What is Clusterization
?
Keyword clustering is combining similar, relevant queries
into groups and using whole groups instead of separate
keywords for site optimization. It helps clean up the
semantic core by dividing it into manageable groups.

43. Semantic Keywords Clusterization

44. Semantic Keywords Clusterization
Why we need it
?
• To create more in-depth articles.
• Group related questions and answers
together for FAQ creation.

45. How To Do That With Python?

46. s

47. Semantic Keywords Clusterization
Upload Your Keywords & Set Your
Cluster Accurac
y

48. Semantic Keywords Clusterization
Upload Your Keywords & Set Your
Cluster Accurac
y

49. Prepare Your Keyword Data

51. Semantic Keywords Clusterization
Detect the character encoding type…

52. Semantic Keywords Clusterization
Detect the character encoding type…

53. Semantic Keywords Clusterization
Detect Your File Delimiter…

54. Create a DataFrame and Organize Columns

56. The Process Continues Until
No More Clusters Are Create

57. e
-> Rename The Clusters To The Shortest Keywor
d

58. Semantic Keywords Clusterization
Move Keyword Columns To The Front

59. Semantic Keywords Clusterization
Final Result

60. Semantic Keywords Clusterization

61. Let’s Create a Ranking
Language Mode

62. s

63. Semantic Keywords Clusterization
Then We Process Our Querie
s

64. Time To Crawl
The Result

68. Ranking Language Model of Keywords

69. Semantic Keywords Clusterization
Results…
LDA - Probability of Keywords To Appear

70. Semantic Keywords Clusterization
Results… Entities

71. Semantic Keywords Clusterization
Results… Co-Occurrence Phrases &
N-Grams

72. Semantic Keywords Clusterization
Results… Topical Modeling

73. Where To Use All This Stuff

76. …
• Better Relevancy Is Achieve
d
• Intent Is Covered - Users Are Happ
y
• Better SEO Visibility For Your
Website

77. Python For Content in SEO
Where To Find These Scripts
:
• https://pypi.org/project/polyfuzz/
• https://query-analysis.herokuapp.com/
• https://github.com/searchsolved/search-solved-public-seo/blob/main/
search_engine_journal/
SEJ_Semantic_Clustering_Tool_by_LeeFootSEO.ipynb
Credits to:
•Lee Foot
•Greg Bernhardt
•Andrea Volpini

78. ABOUT ME
Google Gold Product Exper
t
Head of Content & Semantic
departments @Serpact - agency
winner of a number of prestigious
award
s
2 Times Speaker @ Google
events - Google Dance Zurich
2018, Google Webmaster
Unconference June 2021

79. ABOUT ME
20 Years of Professional
Experience as SEO Specialis
t
4 Years as Python Develope
r
Keen on Data Structures and
Algorithm
s
Go & C++ Enthusiast

81. Thank You For Your Tim