  Sender-receiver interactions, and specifically persuasion games, are widely
researched in economic modeling and artificial intelligence. However, in the
classic persuasion games setting, the messages sent from the expert to the
decision-maker (DM) are abstract or well-structured signals rather than natural
language messages. This paper addresses the use of natural language in
persuasion games. For this purpose, we conduct an online repeated interaction
experiment. At each trial of the interaction, an informed expert aims to sell
an uninformed decision-maker a vacation in a hotel, by sending her a review
that describes the hotel. While the expert is exposed to several scored
reviews, the decision-maker observes only the single review sent by the expert,
and her payoff in case she chooses to take the hotel is a random draw from the
review score distribution available to the expert only. We also compare the
behavioral patterns in this experiment to the equivalent patterns in similar
experiments where the communication is based on the numerical values of the
reviews rather than the reviews' text, and observe substantial differences
which can be explained through an equilibrium analysis of the game. We consider
a number of modeling approaches for our verbal communication setup, differing
from each other in the model type (deep neural network vs. linear classifier),
the type of features used by the model (textual, behavioral or both) and the
source of the textual features (DNN-based vs. hand-crafted). Our results
demonstrate that given a prefix of the interaction sequence, our models can
predict the future decisions of the decision-maker, particularly when a
sequential modeling approach and hand-crafted textual features are applied.
Further analysis of the hand-crafted textual features allows us to make initial
observations about the aspects of text that drive decision making in our setup
