Deep learning in finance assessing twitter sentiment impact and prediction on stocks

• DOI: 10.7717/peerj-cs.2018
• Publication Date: 2024-05-10T07:24:45Z
• Authors: Kaifeng Guo; Haoling Xie
• Keywords: Sentiment Analysis; Stock (firearms); Realm

The widespread adoption of social media platforms has led to an influx data that reflects public sentiment, presenting a novel opportunity for market analysis. This research aims quantify the correlation between fleeting sentiments expressed on and measurable fluctuations in stock market. By adapting pre-existing sentiment analysis algorithm, we refined model specifically evaluating tweets associated with financial markets. was trained validated against comprehensive dataset stock-related discussions Twitter, allowing identification subtle emotional cues may predict changes prices. Our quantitative approach methodical testing have revealed statistically significant relationship Twitter subsequent activity. These findings suggest machine learning algorithms can be instrumental enhancing analytical capabilities experts. article details technical methodologies used, obstacles overcome, potential benefits integrating learning-based into realm economic forecasting.