  Social surveys have been widely used as a method of obtaining public opinion.
Sometimes it is more ideal to collect opinions by presenting questions in
free-response formats than in multiple-choice formats. Despite their
advantages, free-response questions are rarely used in practice because they
usually require manual analysis. Therefore, classification of free-format texts
can present a formidable task in large-scale surveys and can be influenced by
the interpretations of analysts. In this study, we propose a network-based
survey framework in which responses are automatically classified in a
statistically principled manner. This can be achieved because in addition to
the texts, similarities among responses are also assessed by each respondent.
We demonstrate our approach using a poll on the 2016 US presidential election
and a survey taken by graduates of a particular university. The proposed
approach helps analysts interpret the underlying semantics of responses in
large-scale surveys.
