Sentiment analysis and random forest to classify LLM versus human source
  applied to Scientific Texts
Javier J. Sanchez-Medina
After the launch of ChatGPT v.4 there has been a global vivid discussion on
the ability of this artificial intelligence powered platform and some other
similar ones for the automatic production of all kinds of texts, including
scientific and technical texts. This has triggered a reflection in many
institutions on whether education and academic procedures should be adapted to
the fact that in future many texts we read will not be written by humans
(students, scholars, etc.), at least, not entirely. In this work it is proposed
a new methodology to classify texts coming from an automatic text production
engine or a human, based on Sentiment Analysis as a source for feature
engineering independent variables and then train with them a Random Forest
classification algorithm. Using four different sentiment lexicons, a number of
new features where produced, and then fed to a machine learning random forest
methodology, to train such a model. Results seem very convincing that this may
be a promising research line to detect fraud, in such environments where human
are supposed to be the source of texts.