Squid Game Season 2: Seven Text Analyses

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.