School of Foreign Studies, Northwestern Polytechnical University, Xi’an, Shaanxi, China
Email: heboyi@mail.nwpu.edu.cn (B. H.); sunyu@nwpu.edu.cn (Y.S.); jolin@mail.nwpu.edu.cn (Q.L.)
*Corresponding author
Manuscript received January 9, 2026; accepted March 11, 2026; published May 20, 2026
Abstract—This study takes the Nebula Award-winning Sci-Fi work
Story of Your Life by Chinese-American author Ted Chiang, along with its published Chinese translation and two AI-generated Chinese translations, as research objects. It adopts a DH method of CDR, uses Voyant Tools for text visualization, and employs AI-written Python code with affect dictionaries to explore translation strategy orientations based on nodes with significant affect intensity differences. The results show that the original text has the highest lexical density, the Doubao translation has the longest average sentence length, and the DeepSeek translation has the best readability. The original text presents “high positivity-high negativity” tension, the published translation forms a “moderate intensity-dominated” balanced structure via affect adjustment, and AI translations show a “low positivity-low negativity” flat structure. In terms of affect flow, the original text has the largest fluctuation range, the published translation has an earlier peak, and AI translations suffer from reduced tension due to elevated troughs. Strategy-wise, the published translation adopts “free translation–domestication”, DeepSeek tends to “literal translation–simplification”, and Doubao features “combining literal and free translation”. This study expands the methodological path for literary translation research from the DH perspective.
Key Words—affect word intensity, digital humanities,
Story of Your Life, translation strategy
Cite: Boyi He, Yu Sun, and Qionglin Liu, "A Comparative Study on Translation Strategy Orientations of Story of Your Life Based on Affect Intensity,"
International Journal of Languages, Literature and Linguistics, vol. 12, no. 2, pp. 123-127, 2026.
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).