Stock trend prediction using sentiment analysis

These days, the vast amount of data generated on the Internet is a new treasure trove for investors. They can utilize text mining and sentiment analysis techniques to reflect investors' confidence in specific stocks in order to make the most accurate decision. Most previous research just sums up the text sentiment score on each natural day and uses such aggregated score to predict various stock trends. However, the natural day aggregated score may not be useful in predicting different stock trends. Therefore, in this research, we designed two different time divisions: 0:00t -0:00t+1 and 9:30t -9:30t+1 to study how tweets and news from the different periods can predict the next-day stock trend. 260,000 tweets and 6,000 news from Service stocks (Amazon, Netflix) and Technology stocks (Apple, Microsoft) were selected to conduct the research. The experimental result shows that opening hours division (9:30t -9:30t+1) outperformed natural hours division (0:00t -0:00t+1).

Automated summary

Nowadays, the vast amount of data generated on the Internet has become a valuable resource for investors. By using text mining and sentiment analysis techniques, investors can accurately assess confidence in specific stocks to make informed decisions. In this study, two different time divisions were designed to analyze the predictive power of tweets and news on next-day stock trends. The results indicated that the opening hours division outperformed the natural hours division.