![]() Although no conclusions can be drawn from this step alone, I wanted to find the 20 most common words in the book as a stepping stone for future investigation. This preprocessing step was essential to my analysis.įinding the most common words was useful in setting the pace for the rest of the analysis. I then used the NLTK library to tokenize, remove stop words and lemmatize the data. I used the Regex library to strip the text file of digits and newline characters. Key steps: Text Preprocessing, Tokenization, Lemmatization & Topic Modeling Libraries used: NLTK, Gensim, PyLDAvis & Spacy Tools used: Python 3.9 in Jupyter notebooks Analyzing 40,867 words and 207,012 characters (no spaces), here is what I found: I bought an e-version of his book and converted it to a text file. He makes large concepts so digestible that by the end of the book you want to get up and start brainstorming new business ideas.Īfter finishing the business book phenomenon, I decided to apply text analysis and NLP principles to analyze Zero to One. ![]() He slashes the education system for numbing curiosity. He demonstrates slim profit-margins by analyzing the highly competitive Manhattan restaurant marketplace and extols the omnipotent Monopoly in business. Thiel emphasizes the importance of product differentiation in a globalized society. Known as the ultimate startup playbook and less-so as a political manifesto, Zero to One by Peter Thiel takes you on a journey from his Law degree at Stanford to his ultimate success at PayPal and beyond. ![]() “Brilliant thinking is rare, but courage is in even shorter supply than genius.” - Zero to One by Peter Thiel.
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