A Study on Convergence of Text Analysis Techniques for Social Science Research in the Age of Big Data

Abstract

In this paper, we do not only propose appropriate application methods of the semantic network analysis technique, topic modeling technique and text clustering technique, which are techniques that can analyze text document sets but also the convergence application methods between each techniques. Appropriate application methods for the text analysis techniques were presented by dividing exploratory analysis process and modeling process for analyzing the text document set. Semantic network analysis would be not only suitable for exploratory analysis of the entire text document set, but also applicable to performance improvement of topic modeling and text clustering. The meaning of the topic could be interpreted more clearly by applying semantic network analysis secondarily to the document set of each topic derived by topic modeling. It can be used for text cluster interpretation by applying semantic network analysis to the document set of each cluster, which is the result of text cluster analysis. When deriving an appropriate number of topics, a more accurate number of topics could be derived by adding a silhouette analysis along with the degree of perplexity and coherence. New social science research can be expected to be revitalized by enabling more accurate analysis of online text documents using the method proposed in this paper.