论文标题
股票交易的多类情感预测
Multiclass Sentiment Prediction for Stock Trading
论文作者
论文摘要
Python用于下载和格式化Newsapi文章数据，该数据与400个公开交易的低帽子有关。生物技术公司。众包被用来标记这些数据的一个子集，然后训练和评估各种模型，以对每个公司的公众情绪进行分类。然后，最佳性能模型被用来表明完全摆脱公共情绪的交易可以提供市场折磨的回报。
Python was used to download and format NewsAPI article data relating to 400 publicly traded, low cap. Biotech companies. Crowd-sourcing was used to label a subset of this data to then train and evaluate a variety of models to classify the public sentiment of each company. The best performing models were then used to show that trading entirely off public sentiment could provide market beating returns.
