Quantitative questions on big data in translation studies

Abstract

As corpus-based translation studies continues to expand, researchers have employed data analytic techniques from neighbouring disciplines, such as corpus linguistics, to explore a wider variety of research questions. The field has evolved from early frequency-based approaches to corpus-based translation studies to now include more advanced statistical analyses to understand the complex web of variables encapsulated by the translation process. Big data analytic techniques that originated in data analytics and related quantitative fields could be usefully applied to research questions in translation and interpreting studies. To assess their applicability, this article first outlines what distinguishes big data from general corpora in translation and interpreting studies, identifying how data volume, variety, and velocity are applicable properties to be considered in corpus-based translation and interpreting studies research. Then, the article presents three types of big data analysis techniques, namely crosslingual and multilingual data analysis, sentiment analysis, and visual analysis. These analyses are presented in conjunction with potential research areas that would benefit from these complementary analytical approaches. The article concludes with a discussion of the implications of big data analytics in corpus translation studies, while charting the trajectory of a more quantitative, corpus-based approach to translation studies.