Communications in Humanities Research
- The Open Access Proceedings Series for Conferences
Vol. 24, 03 January 2024
* Author to whom correspondence should be addressed.
This paper focuses on the language usage in the GameStop (GME) share rally in January 2021. Specifically, I analyze how comments in a leading post of Reddit’s r/wallstreetbets subreddit reflect the reddit user community’s sentiment and emotion with various linguistic expressions. I analyzed the topics of the comments using topic modeling with Latent Dirichlet allocation algorithm, which objectively identified representative topics in the comments. Results showed that there were six clusters of topics, including two of the topic clusters that represented words that were highly correlated with stock market behaviors, such as “buy” and “sell”, one cluster that suggested objective analyses of the stock market, and one cluster that presented complaining emotions. The findings indicate a direct correlation and impact between the sentiments of comments and the performance of the stock market.
sentiment analysis, GameStop, social media
1. Ahmad, K., Han, J., Hutson, E., Kearney, C., & Liu, S. (2016). Media-expressed negative tone and firm-level stock returns. Journal of Corporate Finance, 37, 152-172.
2. Dong, H., & Gil-Bazo, J. (2020). Sentiment stocks. International Review of Financial Analysis, 72, 101573.
3. Hu, D., Jones, C. M., Zhang, V., & Zhang, X. (2021). The rise of reddit: How social media affects retail investors and short-sellers’ roles in price discovery. Available from SSRN: https://ssrn.com/abstract=3807655.
4. Long, S., Lucey, B., Xie, Y., & Yarovaya, L. (2022). “I Just Like the Stock”: The Role of Reddit Sentiment in the GameStop Share Rally. Financial Review.
5. Anand, A., & Pathak, J. (2022). The role of Reddit in the GameStop short squeeze. Economics Letters, 211, 110249.
6. Boe B. (2023). PRAW: The Python Reddit API Wrapper. https://github.com/praw-dev/praw/ [Online; accessed 2023-06-29].
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).