LSTM Neural Network with Emotional Analysis for Prediction of Stock Price

By Q. Zhuge, Lingyu Xu, Gaowei Zhang

Subfields: Sentiment & Opinion Analysis, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Sentiment & Opinion Analysis

1. Shotaro Minami
2. A. Ozbayoglu
3. Omer Berat Sezer
4. M. Ugur Gudelek

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Shotaro Minami
5. Weiwei Jiang
6. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “In another study [102], the authors used the price time series and emotional data from text posts for predicting the stock opening price of the next day with LSTM network.”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[153] SCI prices 2008-2015 OCHL of change rate, price Emotional Analysis +”
• Predicting Equity Price with Corporate Action Events Using LSTM-RNN (2018, methodology): “[3] made prediction about the opening stock price of individual stocks considering emotion data acquired from the Shanghai General Index and the”