TITLE: A Tweet-based Dataset for Company-Level Stock Return Prediction 

ABSTRACT: 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.
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.
A Tweet-based Dataset for Company-Level Stock Return Prediction
http://arxiv.org/abs/2006.09723v1