Abstract:

Content analysis of 25 years of research papers emanating from Antarctic science research was done. Neural network based algorithm-CATPAC was used for the analysis. 10942 research articles published in Science Citation Indexed (SCI) journals were used for this study. Normalized co-word matrix from 35 most-used significant words were used to study the semantic association between the words. Structural equivalence blocks were constructed from the 35 most-used words. Four-block model solution was found to be optimum. The density table of words was dichotomized using the mean density of the table to derive the binary matrix, which was used to construct the network map. Network maps represent thematic character of the blocks. The blocks showed preferred connection in establishing semantic relationship with other blocks, characterizing thematic composition of Antarctic science research. The analysis provided an analytical framework for carrying out dynamic analysis of the content of articles.

Key words: Antarctica, content analysis, thematic analysis, scientometrics, neural network, co-occurrence, co-word, social network analysis