A Corpus-based Metaphor Study of Annual Reports: Semantic Fields and Metaphors
- DOI
- 10.2991/978-2-38476-265-1_4How to use a DOI?
- Keywords
- annual report; semantic field; BYD; Tesla
- Abstract
Annual reports are documents that disclose a company’s finances, operations, development, social responsibility and other important information. In order to maintain a positive image of the company and enhance investors’ confidence, annual reports tend to adopt a stable discourse structure to express attitudes in indirect forms. Metaphorical analysis of corporate annual reports is a process that requires comprehensive consideration of external environment, professional knowledge and critical thinking. This paper uses the corpus platform Wmatrix 5.0 to analyze annual reports of BYD and Tesla in 2021 and 2022, which are from official websites respectively. This paper follows the detailed steps in the metaphorical identification procedure (MIP) to identify metaphors in each corpus, with METALUDE as the reference source. Accordingly, this study concludes that BYD tends to manipulate different human traits metaphors while Tesla tends to manipulate natural factors including weather and plants. In terms of content, BYD shows an economic recovery while Tesla shows a deep blow.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xiaoshuang Gong PY - 2024 DA - 2024/07/18 TI - A Corpus-based Metaphor Study of Annual Reports: Semantic Fields and Metaphors BT - Proceedings of the 5th International Conference on Language, Art and Cultural Exchange (ICLACE 2024) PB - Atlantis Press SP - 18 EP - 24 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-265-1_4 DO - 10.2991/978-2-38476-265-1_4 ID - Gong2024 ER -