seo

seo
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I believe they work.
I was recently purchasing backlinks and gone through multiple threads.
I noticed few of the users who purchased claiming they got results.
I have yet to see results from the package i purchased.
That’s why I’ve become fascinated with how real data scientists are using Python, NLP, and NLU to do this type of research.

Put simply, all I’m doing here is leveraging tried and tested methods for linguistic analysis and finding a way to apply them in a way that is relevant to SEO.

For the majority of this article, I’ll be talking about the SERPs, but as I’ll explain at the end, this is just scratching the surface of what is possible (and that’s what makes this so exciting!).

Cleaning Text for Analysis

At this point, I should point out that a very important prerequisite of this type of analysis is ‘clean text’. This type of ‘pre-processing’ is essential in ensuring you get a good quality set of results.

While there are lots of great resources out there about preparing text for analysis, for the sake of levity, you can assume that my text has been through most or all of the below processes:

Lower case: The methods I mention below are case sensitive, so making all the copy we use lower case will avoid duplication (if you didn’t do this, ‘yoga’ and ‘Yoga’ would be treated as two different words)
Remove punctuation: Punctuation doesn’t add any extra information for this type of analysis, so we’ll need to remove it from our corpus
Remove stop words: ‘Stop words’ are commonly occurring words within a corpus that add no value to our analysis. In the examples below, I’ll be using predefined libraries from the excellent NLTK or spaCy packages to remove stop words.
Spelling correction: If you’re worried about incorrect spellings skewing your data, you can use a Python library like TextBlob that offers spelling correction
Tokenization: This process will convert our corpus into a series of words. For example, this:
([‘This is a sentence’])

will become:

([‘this’, ‘is’, ‘a’, ‘sentence’])

Stemming: This refers to removing suffixes like ‘-ing’, ‘-ly’ etc. from words and is totally optional
Lemmatization: Similar to ‘stemming,’ but rather than just removing the suffix for a word, lemmatization will convert a word to its root (e.g. “playing” becomes “play”). Lemmatization is often preferred to stemming.
This might all sound a bit complicated, but don’t let it dissuade you from pursuing this type of research.

I’ll be linking out to resources throughout this article which break down exactly how you apply these processes to your corpus.