  We apply techniques in natural language processing, computational
linguistics, and machine-learning to investigate papers in hep-th and four
related sections of the arXiv: hep-ph, hep-lat, gr-qc, and math-ph. All of the
titles of papers in each of these sections, from the inception of the arXiv
until the end of 2017, are extracted and treated as a corpus which we use to
train the neural network Word2Vec. A comparative study of common n-grams,
linear syntactical identities, word cloud and word similarities is carried out.
We find notable scientific and sociological differences between the fields. In
conjunction with support vector machines, we also show that the syntactic
structure of the titles in different sub-fields of high energy and mathematical
physics are sufficiently different that a neural network can perform a binary
classification of formal versus phenomenological sections with 87.1% accuracy,
and can perform a finer five-fold classification across all sections with 65.1%
accuracy.
