Sentiment Analysis Towards Bankruptcy of Silicon Valley Bank: Twitter-Based Study

This study aims to examine the impact of Twitter-based investor's sentiment on the bankruptcy of Silicon Valley Bank (SVB) using a big data approach. The concerns of investors over the bankruptcy of SVB spread rapidly through social media therefore it provides an exceptional setting to explore the role of social media in SVB's collapse. To this end, we employed TextBlob, a Python library for analyzing the sentiments associated with the tweets corresponding to the Silicon Valley Bank collapse. Two hashtags, namely #SiliconValleyBank and #SiliconValleyBankCollapse were used to check the polarity and subjectivity of the tweets. In the case of #SiliconValleyBank, the subjectivity values indicate a strong interest of the investors in the topic backed by facts. Intriguingly, in the case of #SiliconValleyBankCollapse, the investor's sentiment exhibits more objectivity as compared to the #SiliconValleyBank. The inclusion of the keyword, 'collapse', possibly led to more deep and factual comments from investors about SVB's bankruptcy. The insights of this study can assist policymakers in crafting programs and policies that effectively address the role of social media and investor sentiments in financial systems in general. This would enable the banking industry to avoid potential bankruptcy.