  The acquisition of survey responses is a crucial component in conducting
research aimed at comprehending public opinion. However, survey data collection
can be arduous, time-consuming, and expensive, with no assurance of an adequate
response rate. In this paper, we propose a pioneering approach for predicting
survey responses by examining quotations using machine learning. Our
investigation focuses on evaluating the degree of favorability towards the
United States, a topic of interest to many organizations and governments. We
leverage a vast corpus of quotations from individuals across different
nationalities and time periods to extract their level of favorability. We
employ a combination of natural language processing techniques and machine
learning algorithms to construct a predictive model for survey responses. We
investigate two scenarios: first, when no surveys have been conducted in a
country, and second when surveys have been conducted but in specific years and
do not cover all the years. Our experimental results demonstrate that our
proposed approach can predict survey responses with high accuracy. Furthermore,
we provide an exhaustive analysis of the crucial features that contributed to
the model's performance. This study has the potential to impact survey research
in the field of data science by substantially decreasing the cost and time
required to conduct surveys while simultaneously providing accurate predictions
of public opinion.

Computers and Society