The Overview of The Methods of Textual Analysis
DOI:
https://doi.org/10.47134/innovative.v3i4.130Keywords:
Textual Analysis, Linguistic Competence, Pragmatic Analysis, Corpus Linguistics, Cognitive Metaphorical Analysis, PhraseologyAbstract
Textual analysis, an integral part of linguistics, encompasses a variety of methods for studying text structures, meanings, and functions. This article provides a comprehensive overview of the methodologies employed in textual analysis, including componential analysis, pragmatic analysis, cognitive metaphorical analysis, and corpus linguistic analysis. It also examines the interdisciplinary applications of these methods in language teaching, cultural studies, and computational linguistics. By integrating theoretical and practical perspectives, the article highlights the contributions of textual analysis to understanding the multifaceted nature of texts and fostering linguistic competence.
References
Alzaabi, F. R. (2024). A Review of Recent Advances, Challenges, and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods. IEEE Access, 12, 30907–30927. https://doi.org/10.1109/ACCESS.2024.3369906
Brooks, D. (2024). Elements of Long-Term Care That Promote Quality of Life for Indigenous and First Nations Peoples: A Mixed Methods Systematic Review. Gerontologist, 64(1). https://doi.org/10.1093/geront/gnac153
Beaugrande, R. A., & Dressler, W. U. (1981). Introduction to text linguistics. Longman.
Canale, M., & Swain, M. (1980). Theoretical bases of communicative approaches to second language teaching and testing. Applied Linguistics, 1(1), 1–47.
Fiedler, S. (2007). English phraseology: A coursebook. Narr Francke Attempto Verlag.
Chen, Q. (2024). A Word-Level Adversarial Attack Method Based on Sememes and an Improved Quantum-Behaved Particle Swarm Optimization. IEEE Transactions on Neural Networks and Learning Systems, 35(11), 15210–15221. https://doi.org/10.1109/TNNLS.2023.3283308
Galperin, I. R. (1981). Stylistics. Higher School Publishing.
Harris, Z. (1952). Discourse analysis. Language, 28(1), 1–30.
Gryaznova, E. (2024). Machine Learning and Philology: An Overview of Methods and Applications. Springer Geography, 69–84. https://doi.org/10.1007/978-3-031-50609-3_6
Irene, S. (2024). Person search over security video surveillance systems using deep learning methods: A review. Image and Vision Computing, 143. https://doi.org/10.1016/j.imavis.2024.104930
Lakoff, G., & Johnson, M. (1980). Metaphors we live by. University of Chicago Press.
Landow, G. P. (2006). Hypertext 3.0: Critical theory and new media in an era of globalization. Johns Hopkins University Press.
Leech, G. N. (1983). Principles of pragmatics. Longman.
Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
Lee, M. (2024). Effects of textual enhancement and task manipulation on L2 learners’ attentional processes and grammatical knowledge development: A mixed methods study. Language Teaching Research, 28(4), 1552–1571. https://doi.org/10.1177/13621688211034640
Matveeva, T. (2024). Categorical-textual Analysis of a Speech Work: Rationale and Application of the Method. Quaestio Rossica, 12(3), 901–920. https://doi.org/10.15826/qr.2024.3.915
Maslova, V. A. (2001). Linguoculturology. Nauka.
Nida, E. A. (1975). Componential analysis of meaning: An introduction to semantic structures. University of California Press.
Patrício, C. (2024). Explainable Deep Learning Methods in Medical Image Classification: A Survey. ACM Computing Surveys, 56(4). https://doi.org/10.1145/3625287
Rabetino, R. (2024). Digital service innovation (DSI): a multidisciplinary (re)view of its origins and progress using bibliometric and text mining methods. Journal of Service Management, 35(2), 176–201. https://doi.org/10.1108/JOSM-12-2022-0375
Sinclair, J. (1991). Corpus, concordance, collocation. Oxford University Press.
Sveinson, K. (2024). Reviewing and problematizing methods and analytical strategies of discourse analysis in sport, exercise, and physical education studies. International Review for the Sociology of Sport, 59(2), 298–317. https://doi.org/10.1177/10126902231200369
Zarandi, A. K. (2024). A survey of aspect-based sentiment analysis classification with a focus on graph neural network methods. Multimedia Tools and Applications, 83(19), 56619–56695. https://doi.org/10.1007/s11042-023-17701-y
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Tukhtakhodjaeva Zulfiya
This work is licensed under a Creative Commons Attribution 4.0 International License.
