Text Classification and its Effect on EFL University Students’ Performance in Literary Analysis
DOI:
https://doi.org/10.25130/Lang.9.4.P2.19Keywords:
Text ClassificationEFL Students, Literary Analysis, Experimental studyAbstract
The current study aims at finding out the effect of text classification of EFL university students’ performance in literary analysis. The sample of the present study consists of 60 students from Department of English Language at College of Arts / University of Tikrit. Students are divided randomly into two groups: the experimental(EG) which represented in (Section B) and the control (CE) which represented in (Section A), each class consists of 30 students. Both groups are matched in terms of parental academic attainment, age, previous year scores in poetry and the+ pretest. The study has lasted fourteen weeks during the first semester of the academic year 2024/2025. To achieve the aims and verify its’ hypotheses, Non-randomized Control Group Pretest and Post-test Design has been adoptedin this study. The collected data have been statistically analyzed by using different statistical means. The results indicate that the text classification has beensignificantly enhanced the performance of EFL university students’ literary analysis.
References
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning (ECML) (pp. 137–142).
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186).
Kim, Y. (2014). Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1746–1751).
Lewis, D. D. (1992). An evaluation of phrasal and clustered representations on a text categorization task. In Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 37–50). ACM.
Fairclough, N. (2010). Critical discourse analysis: The critical study of language (2nd ed.). Routledge.
Yang, Y. (2019). Text classification algorithms: A survey. International Journal of Data Science, 4(2), 123–139.
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). Association for Computational Linguistics.
https://arxiv.org/abs/1810.04805
Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive Bayes text classifiers. In Proceedings of the 20th International Conference on Machine Learning (pp. 616–623).
Silla, C. N., & Freitas, A. A. (2011). A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery, 22(1), 31–72.
https://doi.org/10.1007/s10618-010-0175-9
Zhang, M.-L., & Zhou, Z.-H. (2014). A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 26(8), 1819–1837.
Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys (CSUR), 34(1), 1–47.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning (ECML) (pp. 137–142).
Tsoumakas, G., &Katakis, I. (2007). Multi-label classification: An overview. International Journal of Data Warehousing and Mining, 3(3), 1–13
Halliday, M. A. K., & Hasan, R. (1976). Cohesion in English. Longman.
Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.
Kress, G., & van Leeuwen, T. (2001). Multimodal discourse: The modes and media of contemporary communication. Arnold
Kress, G., & van Leeuwen, T. (2001). Multimodal discourse: The modes and media of contemporary communication. Arnold
Herring, S. C. (2004). Computer-mediated communication on the Internet. Annual Review of Information Science and Technology, 38, 231–264.
Bolter, J. D., & Grusin, R. (1999). Remediation: Understanding new media. MIT Press.
Barthes, R. (1967). Elements of semiology. Hill and Wang.
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.