  We propose \textbf{occ2vec}, a principal approach to representing
occupations, which can be used in matching, predictive and causal modeling, and
other economic areas. In particular, we use it to score occupations on any
definable characteristic of interest, say the degree of \textquote{greenness}.
Using more than 17,000 occupation-specific text descriptors, we transform each
occupation into a high-dimensional vector using natural language processing.
Similar, we assign a vector to the target characteristic and estimate the
occupational degree of this characteristic as the cosine similarity between the
vectors. The main advantages of this approach are its universal applicability
and verifiability contrary to existing ad-hoc approaches. We extensively
validate our approach on several exercises and then use it to estimate the
occupational degree of charisma and emotional intelligence (EQ). We find that
occupations that score high on these tend to have higher educational
requirements. Turning to wages, highly charismatic occupations are either found
in the lower or upper tail in the wage distribution. This is not found for EQ,
where higher levels of EQ are generally correlated with higher wages.
