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 interpretation 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 text, 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. Classifying individual responses in open-ended surveys can be subjective and time-consuming. A network-based survey framework automatically classifies responses in a statistically principled manner.