# Analyzing public sentiment to gauge key stock events and determine volatility in conjunction with time and options premiums

arXiv: https://arxiv.org/abs/2502.05403 · HTML: https://mlsanity.com/paper/2502.05403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 8 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond](https://mlsanity.com/paper/2104.12250/) (2021)
- [Sentiment Analysis of Twitter Data for Predicting Stock Market Movements](https://mlsanity.com/paper/1610.09225/) (2016)
- [Leveraging Large Language Models to Detect Influence Campaigns in Social Media](https://mlsanity.com/paper/2311.07816/) (2023)
- [Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model](https://mlsanity.com/paper/2206.06606/) (2022)
- [FinReport: Explainable Stock Earnings Forecasting via News Factor Analyzing Model](https://mlsanity.com/paper/2403.02647/) (2024)
- [Predicting the Effects of News Sentiments on the Stock Market](https://mlsanity.com/paper/1812.04199/) (2018)
- [Using Twitter Attribute Information to Predict Stock Prices](https://mlsanity.com/paper/2105.01402/) (2021)
- [Constructing Financial Sentimental Factors in Chinese Market Using Natural Language Processing](https://mlsanity.com/paper/1809.08390/) (2018)
- [Evaluating Impact of Social Media Posts by Executives on Stock Prices](https://mlsanity.com/paper/2211.01287/) (2022)

## Abstract (arXiv)

Analyzing stocks and making higher accurate predictions on where the price is heading continues to become more and more challenging therefore, we designed a new financial algorithm that leverages social media sentiment analysis to enhance the prediction of key stock earnings and associated volatility. Our model integrates sentiment analysis and data retrieval techniques to extract critical information from social media, analyze company financials, and compare sentiments between Wall Street and the general public. This approach aims to provide investors with timely data to execute trades based on key events, rather than relying on long-term stock holding strategies. The stock market is characterized by rapid data flow and fluctuating community sentiments, which can significantly impact trading outcomes. Stock forecasting is complex given its stochastic dynamic. Standard traditional prediction methods often overlook key events and media engagement, focusing its practice into long-term investment options. Our research seeks to change the stochastic dynamic to a more predictable environment by examining the impact of media on stock volatility, understanding and identifying sentiment differences between Wall Street and retail investors, and evaluating the impact of various media networks in predicting earning reports.