Squid Game Season 2: Seven Text Analyses

Authors

  • Namkil Kang

    College of Liberal Arts, Far East University, 76-32 Daehak-gil, Gamgok-myeon, Eumseong-gun, Chungbuk, Republic of Korea

  • Eun Hee Kim

    College of Liberal Arts, Far East University, 76-32 Daehak-gil, Gamgok-myeon, Eumseong-gun, Chungbuk, Republic of Korea

DOI:

https://doi.org/10.30564/fls.v7i4.8902
Received: 28 February 2025 | Revised: 18 March 2025 | Accepted: 21 March 2025 | Published Online: 30 March 2025

Abstract

This article analyzes the linguistic topics and themes present in 33 Google articles about Squid Game Season 2, utilizing big data techniques in Python. Seven different methods were employed to uncover linguistic patterns, identifying season as the most frequently used keyword, followed by game, squid, Netflix, and games. The prominence of season  emerged as both a central topic and a dominant theme in the articles. Other significant terms included competition, character, hit, critics, contest, event, and director, all of which were frequently mentioned. The analysis also revealed strong connections between key terms, such as people and game, season and game, as well as Lee and game, Hwang and game, and Netflix and game, further reinforcing the linguistic themes. Sentiment analysis showed a predominantly positive tone toward Squid Game Season 2, with a sentiment score of +146 for positive terms compared to 17 for negative terms. In conclusion, the study highlighted game, squid, Netflix, and games as key linguistic topics and themes. The use of topic modeling, keyword analysis, and network analysis provided valuable insights into the linguistic structure of the articles, contributing to the broader field of linguistics. The linguistic significance of this research lies in the use of big data analysis techniques to conduct seven distinct text analyses, providing deeper insights into the major linguistic themes and topics within the text.

Keywords:

Term Frequency; Word Cloud; Network; Topic; Similarity; Sentimental Anaysis

References

[1] Baker, P., 2006. Using Corpora in Discourse Analysis. Continuum: London, UK. pp. 1–224.

[2] Baker, P., McEnery, T., 2005. A Corpus-Based Study of Euphemism and Dysphemism. Journal of Pragmatics. 37(6), 955–967.

[3] Biber, D., Conrad, S., Reppen, R., 1998. Corpus Linguistics: Investigating Language Structure and Use. Cambridge University Press: Cambridge, UK. pp. 1–320.

[4] Blei, D.M., Ng, A.Y., Lafferty, J.D., 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research. 3, 993–1022.

[5] Charles, M., 2011. A Corpus-Based Study of Academic Writing. Journal of English for Academic Purposes. 10(3), 213–226.

[6] Jurafsky, D., Martin, J.H., 2023. Speech and Language Processing: An Introduction to Natural LanguageProcessing, Computational Linguistics, and Speech Recognition (3rd ed.). Pearson: London, UK. pp. 1–1024.

[7] Evison, J., Turner, K., 2014. Using Corpora for Text Analysis in Research. Language Resources and Evaluation. 48(1), 69–85.

[8] Cheng, X., Jin, Z., 2020. Text Mining: A Review of Recent Research and Its Applications. International Journal of Computer Applications. 176(13), 18–25.

[9] Grabe, W., Kaplan, R.B., 1996. Theory and Practice of Writing: An Applied Linguistic Perspective. Longman: London, UK. pp. 1–336.

[10] Gries, S.T., 2009. Quantitative Approaches in Usage-based Linguistics. In Handbook of Cognitive Linguistics. Routledge: London, UK. pp. 178–195.

[11] Halliday, M.A.K., Matthiessen, C., 2014. An Introduction to Functional Grammar. Routledge: New York, NY, USA. pp. 1–786.

[12] Liu, B., 2012. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. 5(1), 1–167.

[13] Lott, D., 2009. The Use of Corpora in the Teaching of English for Academic Purposes. English for Specific Purposes. 28(2), 119–130.

[14] Manning, C.D., Schütze, H., 1999. Foundations of Statistical Natural Language Processing. MIT Press: Cambridge, MA, USA. pp. 1–720.

[15] McEnery, T., Hardie, A., 2011. Corpus Linguistics: Method, Theory and Practice. Cambridge University Press: Cambridge, UK. pp. 1–287.

[16] McEnery, T., Wilson, A., 2001. Corpus Linguistics: An Introduction. Edinburgh University Press: Edinburgh, UK. pp. 1–256.

[17] Meyer, C.F., 2009. Introducing English Linguistics. Cambridge University Press: Cambridge, UK. pp. 1–240.

[18] Rayson, P., Garside, R., 2000. Comparing Corpora Using Frequency Profiling. Proceedings of the Workshop on Comparing corpora; October 7, 2000; Hong Kong, China. pp. 1–6.

[19] Sinclair, J., 1991. Corpus, Concordance, Collocation. Oxford University Press: Oxford, UK. pp. 1–180.

[20] Swales, J.M., Feak, C.B., 2004. Academic Writing for Graduate Students: Essential Tasks and Skills. University of Michigan Press: Ann Arbor, MI, USA. pp. 1–440.

[21] Taboada, M., Grieve, J., 2004. Analyzing Sentiment in Text. Computational Linguistics. 30(3), 259–270.

[22] Tognini-Bonelli, E., 2001. Corpus Linguistics at Work. John Benjamins: Amsterdam, The Netherlands. pp. 1–239.

[23] van Dijk, T.A., 1997. Discourse as Structure and Process. Sage Publications: Los Angeles, CA, USA. pp. 1–356.

[24] Sampson, G., 2001. Empirical Linguistics. Continuum: London, UK. pp. 1–226.

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How to Cite

Kang, N., & Kim, E. H. (2025). Squid Game Season 2: Seven Text Analyses. Forum for Linguistic Studies, 7(4), 214–225. https://doi.org/10.30564/fls.v7i4.8902