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

06/17/2020
by   Karolina Sowinska, et al.
0

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.

READ FULL TEXT
research
10/28/2016

Sentiment Analysis of Twitter Data for Predicting Stock Market Movements

Predicting stock market movements is a well-known problem of interest. N...
research
03/30/2023

Taureau: A Stock Market Movement Inference Framework Based on Twitter Sentiment Analysis

With the advent of fast-paced information dissemination and retrieval, i...
research
05/13/2023

Trillion Dollar Words: A New Financial Dataset, Task Market Analysis

Monetary policy pronouncements by Federal Open Market Committee (FOMC) a...
research
08/25/2023

Automatic Historical Stock Price Dataset Generation Using Python

With the dynamic political and economic environments, the ever-changing ...
research
12/17/2018

Hateminers : Detecting Hate speech against Women

With the online proliferation of hate speech, there is an urgent need fo...
research
10/31/2022

Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty

Machine learning is an increasingly popular tool with some success in pr...
research
03/31/2020

Learning to Ask Medical Questions using Reinforcement Learning

We propose a novel reinforcement learning-based approach for adaptive an...

Please sign up or login with your details

Forgot password? Click here to reset