If you convert words into vectors, you can do interesting things with them. You can compare the topics in a book, make better translations and tell if a sentence is positive or negative. Python libraries like gensim and spaCy make it easy to play with this for fun, profit or social science.
This talk introduces Computational Social Science as a new research discipline, gives a brief introduction to Natural Language Processing and explains how Word Vector Representations are computed and how to use them in Python. Word Vector Representations like word2vec encode semantic relationships like gender and "is the capital city of". This makes it easy to find similar words and compare them visually. To illustrate this, I am using the gensim and scikit-learn Python libraries to compare my own Google searches from 2011 and 2014. I will also explain how to use this to compare the topics in books and book summaries.