论文标题
基于推文的数据集用于公司级股票回报预测
A Tweet-based Dataset for Company-Level Stock Return Prediction
论文作者
论文摘要
公众舆论影响了事件，特别是与股票市场运动有关的事件，在这些事件中，微妙的提示会影响市场的当地结果。在本文中，我们提出了一个数据集，该数据集允许对基于推文的影响对一，两，三和7天的股票收益进行基于推文的影响。我们的数据集由862，231个带有英语的标记实例组成，我们还向社区发布了85，176个标记实例的清洁子集。我们还使用标准机器学习算法和基于多视图的学习方法提供基准，该方法利用不同类型的功能。我们的数据集，脚本和模型可在以下网址公开获取：https：//github.com/imperialnlp/stockreturnpred。
Public opinion influences events, especially related to stock market movement, in which a subtle hint can influence the local outcome of the market. In this paper, we present a dataset that allows for company-level analysis of tweet based impact on one-, two-, three-, and seven-day stock returns. Our dataset consists of 862, 231 labelled instances from twitter in English, we also release a cleaned subset of 85, 176 labelled instances to the community. We also provide baselines using standard machine learning algorithms and a multi-view learning based approach that makes use of different types of features. Our dataset, scripts and models are publicly available at: https://github.com/ImperialNLP/stockreturnpred.
