A Quantitative Content Analysis of L1 and L2 English Writings Using a Text Mining Approach

This study aimed to explore personal letters written in English as the first language (L1) and the second language (L2) using a text mining approach. The purpose of this study is to examine the usage of emotional expressions in English letters written by native English writers and Japanese learners of English. Among the emotions, affection and condolence are focused on. This study is also interested in confirming whether quantitative content analysis can obtain the same results as those shown by an analysis using a different method. Production experiments were carried out to compare the usage of English emotional expressions by Japanese university students with that of native English writers. All the subjects were instructed to write two kinds of personal letters; a love letter and a letter of condolence, under certain conditions. The contents of the letters were analyzed by means of the “text mining” method. Words frequently used in the letters were obtained, and a co-occurrence network of major words was created to investigate the relationships among words, which helped to understand how the writers expressed their emotions in English. Different linguistic features and patterns were observed in the letters written by the native English writers and those by the Japanese learners of English, respectively. It was also shown that quantitative content analysis supported the results obtained in another study using a different method. The results of this study showed quantitative content analysis is reliable method to better understand texts.