Use of N-grams for Content Optemization

Analyzing N-grams can be done both manually and using tools. However, using tools is generally more efficient and provides more accurate insights.?

Here is how we can conduct an N-gram analysis to determine which content is more likely to rank on the SERPs.

For instance, when I searched "What is n-gram analysis?" on Google:

Content (a), positioned first in the featured snippet, defines:

"An n-gram is a collection of n successive items in a text document, which may consist of words, numbers, symbols, and punctuation. N-gram models are vital in many text analytics applications where word sequences matter, like sentiment analysis, text classification, and text generation."

Here is the URL of the content “A”: https://www.mathworks.com/discovery/ngram.html#:~:text=An%20n%2Dgram%20is%20a,text%20classification%2C%20and%20text%20generation.

While content (b), ranked 21st on Google SERP, elucidates:

"N-grams are all combinations of adjacent words or letters of length n found in your source text. For instance, given the word 'fox', all 2-grams (or “bigrams”) are 'fo' and 'ox'. You can also count the word boundary, expanding the list of 2-grams to include #f, fo, ox, and x#, with # indicating a word boundary."

Here is the URL of the content B: https://stackoverflow.com/questions/18193253/what-exactly-is-an-n-gram

So, why did Google rank content (a) higher than (b)?

Let's analyze the two paragraphs using unigrams (single words) to determine which one is more relevant to the query "What is N-gram analysis?"

Unigram Analysis

Paragraph A Unigrams:

1. An
2. n-gram
3. is
4. a
5. collection
6. of
7. n
8. successive
9. items
10. in
11. text
12. document
13. that
14. may
15. include
16. words
17. numbers
18. symbols
19. and
20. punctuation
21. N-gram
22. models
23. are
24. useful
25. in
26. many
27. text
28. analytics
29. applications
30. where
31. sequences
32. of
33. words
34. are
35. relevant
36. such
37. as
38. in
39. sentiment
40. analysis
41. text
42. classification
43. and
44. text
45. generation

Paragraph B Unigrams:

1. N-grams
2. are
3. simply
4. all
5. combinations
6. of
7. adjacent
8. words
9. or
10. letters
11. of
12. length
13. n
14. that
15. you
16. can
17. find
18. in
19. your
20. source
21. text
22. For
23. example
24. given
25. the
26. word
27. fox
28. all
29. 2-grams
30. or
31. “bigrams”
32. are
33. fo
34. and
35. ox
36. You
37. may
38. also
39. count
40. the
41. word
42. boundary
43. that
44. would
45. expand
46. the
47. list
48. of
49. 2-grams
50. to
51. #f
52. fo
53. ox
54. and
55. x#
56. where
57. denotes
58. a
59. word
60. boundary

Analysis:

Paragraph A consists of 45 unigram components, while Paragraph B contains 60. Notably, the term "n-gram" appears twice in Paragraph A, but only once in Paragraph B. Additionally, the word "analysis" is mentioned once in Paragraph A, whereas it is absent in Paragraph B. Based on this unigram analysis, Paragraph A seems to be more suitable for ranking on SERPs for the keyword "What is N-gram analysis?". However, we will conduct further N-gram analysis to clarify the results.

Now, let's analyze the two paragraphs using bigrams (two adjacent words) to determine which one is more relevant to the query "What is N-gram analysis?"

Bigram Analysis

Paragraph A Bigrams:

1. An n-gram
2. n-gram is
3. is a
4. a collection
5. collection of
6. of n
7. n successive
8. successive items
9. items in
10. in a
11. a text
12. text document
13. document that
14. that may
15. may include
16. include words
17. words, numbers
18. numbers, symbols
19. symbols, and
20. and punctuation
21. N-gram models
22. models are
23. are useful
24. useful in
25. in many
26. many text
27. text analytics
28. analytics applications
29. applications where
30. where sequences
31. sequences of
32. of words
33. words are
34. are relevant
35. relevant, such
36. such as
37. as in
38. in sentiment
39. sentiment analysis
40. analysis, text
41. text classification
42. classification, and
43. and text
44. text generation

Paragraph B Bigrams:

1. N-grams are
2. are simply
3. simply all
4. all combinations
5. combinations of
6. of adjacent
7. adjacent words
8. words or
9. or letters
10. letters of
11. of length
12. length n
13. n that
14. that you
15. you can
16. can find
17. find in
18. in your
19. your source
20. source text
21. For example
22. example, given
23. given the
24. the word
25. word fox
26. fox, all
27. all 2-grams
28. 2-grams (or
29. (or “bigrams”)
30. “bigrams” are
31. are fo
32. fo and
33. and ox
34. You may
35. may also
36. also count
37. count the
38. the word
39. word boundary
40. boundary –
41. – that
42. that would
43. would expand
44. expand the
45. the list
46. list of
47. of 2-grams
48. 2-grams to
49. to #f
50. #f, fo
51. fo, ox
52. ox, and
53. and x#
54. x#, where
55. where #
56. denotes
57. denotes a
58. a word
59. word boundary

Analysis:

1. Upon conducting a unigram analysis of both paragraphs, it's observed that Paragraph A contains 44 bigram components, while Paragraph B has 59.
2. For the search query "What is N-gram analysis?", the primary terms of focus are "n-gram" and "analysis". In Paragraph A, the term "n-gram" appears in phrases such as "An n-gram", "n-gram is", and "N-gram models", totaling three mentions. In contrast, Paragraph B mentions this term only once, as seen in "N-grams are".
3. When we consider the term "analysis", Paragraph A mentions it twice - in "sentiment analysis" and "analysis, text". However, Paragraph B does not include this term at all.
4. Despite Paragraph B having a higher word count than Paragraph A, the latter is richer in user intent. This suggests that Paragraph A is more deserving of a higher ranking on SERPs.

Now, let's break down the two paragraphs using trigrams (three adjacent words) to determine which one better matches the user's query "What is N-gram analysis?"

Trigram Analysis

Paragraph A Trigrams:

1. An n-gram is
2. n-gram is a
3. is a collection
4. a collection of
5. collection of n
6. of n successive
7. n successive items
8. successive items in
9. items in a
10. in a text
11. a text document
12. text document that
13. document that may
14. that may include
15. may include words
16. include words, numbers
17. words, numbers, symbols
18. numbers, symbols, and
19. symbols, and punctuation
20. N-gram models are
21. models are useful
22. are useful in
23. useful in many
24. in many text
25. many text analytics
26. text analytics applications
27. analytics applications where
28. applications where sequences
29. where sequences of
30. sequences of words
31. of words are
32. words are relevant
33. are relevant, such
34. relevant, such as
35. such as in
36. as in sentiment
37. in sentiment analysis
38. sentiment analysis, text
39. analysis, text classification
40. text classification, and
41. classification, and text
42. and text generation

Paragraph B Trigrams:

1. N-grams are simply
2. are simply all
3. simply all combinations
4. all combinations of
5. combinations of adjacent
6. of adjacent words
7. adjacent words or
8. words or letters
9. or letters of
10. letters of length
11. of length n
12. length n that
13. n that you
14. that you can
15. you can find
16. can find in
17. find in your
18. in your source
19. your source text
20. For example, given
21. example, given the
22. given the word
23. the word fox
24. word fox, all
25. fox, all 2-grams
26. all 2-grams (or
27. 2-grams (or “bigrams”)
28. (or “bigrams”) are
29. “bigrams”) are fo
30. are fo and
31. fo and ox
32. You may also
33. may also count
34. also count the
35. count the word
36. the word boundary
37. word boundary –
38. boundary – that
39. – that would
40. that would expand
41. would expand the
42. expand the list
43. the list of
44. list of 2-grams
45. of 2-grams to
46. 2-grams to #f
47. to #f, fo
48. #f, fo, ox
49. fo, ox, and
50. ox, and x#
51. and x#, where
52. x#, where #
53. where # denotes
54. denotes a
55. denotes a word
56. a word boundary

Analysis:

Upon analyzing both Paragraphs A and B, we begin by examining our primary keyword: "What is N-gram analysis?".

In Paragraph A, the term most semantically and lexically related to our keyword is "N-gram". This term appears three times in the following contexts:

1. "An n-gram is"
2. "n-gram is a"
3. "N-gram models are"

In comparison, Paragraph B mentions "N-gram" only once, as seen in "N-grams are simply".

Another significant term from our keyword is "analysis". In Paragraph A, it's mentioned three times:

1. "in sentiment analysis"
2. "sentiment analysis, text"
3. "analysis, text classification"

However, Paragraph B does not include this term at all. This evidence suggests that Paragraph A is more aligned with the keyword and is, therefore, more deserving of a higher ranking compared to Paragraph B.

Let's break down the two paragraphs using 4-grams (four adjacent words) to determine which one is more relevant to the query "What is N-gram analysis?"

4-grams Analysis

Paragraph A 4-grams:

1. An n-gram is a
2. n-gram is a collection
3. is a collection of
4. a collection of n
5. collection of n successive
6. of n successive items
7. n successive items in
8. successive items in a
9. items in a text
10. in a text document
11. a text document that
12. text document that may
13. document that may include
14. that may include words
15. may include words, numbers
16. include words, numbers, symbols
17. words, numbers, symbols, and
18. numbers, symbols, and punctuation
19. N-gram models are useful
20. models are useful in
21. are useful in many
22. useful in many text
23. in many text analytics
24. many text analytics applications
25. text analytics applications where
26. analytics applications where sequences
27. applications where sequences of
28. where sequences of words
29. sequences of words are
30. of words are relevant
31. words are relevant, such
32. are relevant, such as
33. relevant, such as in
34. such as in sentiment
35. as in sentiment analysis
36. in sentiment analysis, text
37. sentiment analysis, text classification
38. analysis, text classification, and
39. text classification, and text
40. classification, and text generation

Paragraph B 4-grams:

1. N-grams are simply all
2. are simply all combinations
3. simply all combinations of
4. all combinations of adjacent
5. combinations of adjacent words
6. of adjacent words or
7. adjacent words or letters
8. words or letters of
9. or letters of length
10. letters of length n
11. of length n that
12. length n that you
13. n that you can
14. that you can find
15. you can find in
16. can find in your
17. find in your source
18. in your source text
19. For example, given the
20. example, given the word
21. given the word fox
22. the word fox, all
23. word fox, all 2-grams
24. fox, all 2-grams (or
25. all 2-grams (or “bigrams”)
26. 2-grams (or “bigrams”) are
27. (or “bigrams”) are fo
28. “bigrams”) are fo and
29. are fo and ox
30. fo and ox. You
31. and ox. You may
32. ox. You may also
33. You may also count
34. may also count the
35. also count the word
36. count the word boundary
37. the word boundary –
38. word boundary – that
39. boundary – that would
40. – that would expand
41. that would expand the
42. would expand the list
43. expand the list of
44. the list of 2-grams
45. list of 2-grams to
46. of 2-grams to #f
47. 2-grams to #f, fo
48. to #f, fo, ox
49. #f, fo, ox, and
50. fo, ox, and x#
51. ox, and x#, where
52. and x#, where #
53. x#, where # denotes
54. where # denotes a
55. denotes a word boundary

Analysis:

Similar to our previous analysis, Paragraph A contains more relevant 4-grams compared to Paragraph B. This suggests that Paragraph A is more deserving of a higher ranking on SERPs.

Further N-gram Analysis:?

When assessing both Paragraphs A and B concerning the keywords "N-gram" and "What is N-gram?", it's insightful to review their respective search rankings. Searching for "N-gram" on Google, Paragraph A, represented by mathworks.com, appears prominently at the top. In stark contrast, Paragraph B doesn't even make it to the first 60 results.

More intriguingly, when we input the keyword “What is N-gram?” into the Google search bar, mathworks.com (representing Paragraph A) secures the second spot, while stackoverflow.com lags behind at the 26th position. It's worth noting that even though stackoverflow.com embeds the exact phrase “What is N-gram?” within its page URL (https://stackoverflow.com/questions/18193253/what-exactly-is-an-n-gram), it still doesn't outperform mathworks.com.

Final Thoughts

So, why can't a website rank higher even with the targeted keyword in its URL? The answer might be simpler than you think: the quality of the content matters immensely.