N-grams help in understanding the structure and patterns in text. N-gram language models find use in various natural language processing applications such as predicting words in a sentence. They can also apply in analysing text for sentiment, and even in search engines to improve results.
But what is an N-gram? The “n” in N-grams stands for the number of items in a sequence. Think of N-grams as chunks of text, for better and granular analysis of data or text. Imagine you have a sentence or a large piece of text, so you break it down into smaller pieces to analyse it better.
You would break up the text into N-grams to make it smaller and easier to analyse and spot patterns.
N-Gram Model – The Basics of N-gram Analysis
The “n” in N-grams stands for the number of items in a sequence. These items can be words, letters, or symbols. N-grams are categorised according to the number of items in the sequence breakdown. For example, in the sentence “I like to breathe”:
If the N-grams analysis breaks down to single words like “I”, “like”, “to”, “breathe”, each item is a unigram or 1-gram. The value of “n” can be any positive integer, allowing for sequences of varying lengths.
- Unigram (1-gram): “I”, “like”, “to”, “dance”
- Bigram (2-gram): “I like”, “like to”, “to dance”
- Trigram (3-gram): “I like to”; “like to dance”
Applications of N-Grams
N-grams are useful in various applications like text prediction, language modeling, and sentiment analysis. By breaking down text into granular chunks it becomes easier to spot patterns and keyword usage.
In SEO, it’s vital to compare one’s content with successful competitors. Reading through the competitors’ content is not enough. One needs to see core keyword usage and content structure. Through N-gram models, we can break down large samples of text and identify keyword usage patterns.
Historical Context of N-Grams
A great way to understand N-grams is by looking at earlier applications like phrase counting or keyword counting tools. One such tool is found here http://www.writewords.org.uk/phrase_count.asp .
For instance, you can specify the length of the phrases, such as 2-word phrases for bigrams or longer for more complex analysis. These tools break down large texts into phrases and tabulate how frequently each phrase or keyword appears.
We have in another article previously outline how phrase counting tools were applied in SEO analysis. However modern tools that use AI have simplified and sped up N-gram analysis. An example of a tool that has made N-gram analysis fast and efficient, is Screaming Frog. It shows how frequently each N-gram is used and how it is used in the text.
N-Gram Analysis Made Easy with AI – Screaming Frog N-grams
Screaming Frog is an application that simplifies N-gram analysis. You can crawl a website or specific page to get N-grams of any URL on the site, ranging from 1-gram to 6-gram breakdowns. This tool provides insights into how often any single N-gram or phrase is used, where it is used (e.g., in headings or body text), and its density in relation to the overall page.
How to Get N-Gram Model Using Screaming Frog
- 1. Configuration:
- Click on Configuration in the menu.
- Select Spider and then Extraction.
- In the HTML section, tick the boxes for Store HTML and Store Rendered HTML.
- Click OK to save your changes.
- 2. Crawl:
- Start a crawl of your website or web page.
- Select the URL/webpage you want to analyze.
- 3. N-Grams Section:
- Click on the arrow on the bottom ribbon to find the N-grams section.
- Click on the N-grams section to reveal your results.
- Select the value or number of N-grams you want to analyze.
- You can also get a visual representation of the N-grams through a word cloud image that you can export and save.
Applying N-Gram Analysis to Improve Your Content
With N-gram analysis, you can easily see how often you are using certain phrases and keywords in your text. This helps you understand their usage in headings, body text, or as anchor text for links. By comparing your N-gram analysis with similar websites in your niche, you can optimize your content accordingly.
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