The language of a CEO, NLP analysis of Steve Jobs commencement speech

The language of a CEO, NLP analysis of Steve Jobs commencement speech

In this article, I will perform text analysis of the famous speech given by Steve Jobs to find which are hist most frequently used words. However, it is not so easy as it may sound. I will explain how to process the words using Natural Language Processing using Spacy.

In this article, I will perform text analysis of the famous speech given by Steve Jobs to find which are hist most frequently used words. However, it is not so easy as it may sound. I will explain how to process the words using Natural Language Processing using Spacy.

Issues with NLP and how to solve them

Using basic programming, there are too many elements of a text stored as a string that will make our results useless:

  • Punctuation
  • Different words with the same root

For example, the sentence:

'I asked my mother if she could buy me cookies. She told me she already bought them.'

After removing punctuation, if I run the algorithm to extract words by frequency, this is the result:

('asked', 1),
('mother', 1),
('buy', 1),
('cookies', 1),
('told', 1),
('bought', 1)

As you can see, the computer makes a big distinction between the verb buy *and *bought. In reality, because they derive from the same verb, should be counted under the same category. How do I solve this problem? By using lemmatization.

Lemmatization

What lemmatization does is iterating through every single word to find its root.

Image for post

Lemmatization of the word ‘change’

This is the result I obtain after lemmatization. As you can see, now the word *buy *is counted twice.

('buy', 2),
('ask', 1),
('mother', 1),
('cookie', 1),
('tell', 1)]

I am ready to write the software and analyze the text I want:

artificial-intelligence steve-jobs naturallanguageprocessing apple spacy

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