Text mining of syntactic complexity in L2 writing: an LDA topic modeling approach

Abstract

Abstract The present study provides an overview of syntactic complexity (SC) in second language (L2) writing research, employing the Latent Dirichlet Allocation (LDA) topic modeling approach. Analyzing 470 abstracts sourced from the WoS Core Collection (2005–2023) and Scopus (1978–2023), this investigation explores the broad spectrum of research themes in SC in L2 writing. By employing perplexity and coherence tests, the study identifies the optimal number of topics in the model as five: writing quality, proficiency, genres, SC development, and task-based conditions. The finding reveals that: (1) In cross-sectional studies, the interplay among writing quality, proficiency levels, and genres often involves studying one variable as reliant on the others. Additionally, there is a growing inclination toward regarding phrasal structures as more dependable complexity indicators. (2) Longitudinal investigations predominantly emphasize the evolution of SC, yet there’s a scarcity of exploration in continuation writing tasks. (3) Regarding measurement metrics, a transition from broader SC evaluations to more intricate assessments is evident, though several studies lack thorough examinations, particularly in continuation tasks.