Lstm neural network with emotional analysis for prediction of stock price. This paper introduces a sophisticated deep learning-based framework, employing Long Short-Term Memory (LSTM) networks to accurately forecast the closing stock prices of leading technology firms—namely Apple, Google, Microsoft, and Amazon—listed on the NASDAQ. This section will describe researches that were related to LSTM neural networks, stock price forecast, text sentiment analysis, and the BERT model in the following. This project explores the use of Long Short-Term Memory (LSTM) networks for time series forecasting in stock market analysis. Engineering Letters, 25, 167-175. See full list on link. . com, and applies Bidirectional L STM and Multi-layer LstM into stock price prediction, which also provides the possibility of parameter setting improvement. To search for new and effective input variables for LSTM neural network, we used stock exchange, Shanghai Composite Index and emotional data as input variables, and then experimental results showed that the proposed 15 input variables can successfully predict the stock opening price. com The primary goal is to develop a robust predictive model for stock price forecasting, leveraging Long Short-Term Memory (LSTM) neural networks and integrating sentiment analysis. LSTM Neural Network with Emotional Analysis for Prediction of Stock Price Qun Zhuge, Lingyu Xu and Gaowei Zhang Abstract—Time series forecasting is an important and widely known topic in the research of statistics, with the forecasting of stock opening price being the most crucial element in the entire forecasting process. It can effectively predict stock market prices by handling data with multiple input and output timesteps. This paper uses linear regression models and LSTM models based on machine learning to predict the stock price of Amazon. Apr 22, 2025 · The complexities of stock price data, characterized by its nonlinearity, non-stationarity, and intricate spatiotemporal patterns, make accurate prediction a substantial challenge. and Zhang, G. W. , Xu, L. Jan 1, 2017 · The proposed model consists of two parts, namely the emotional analysis model and the long short-term memory (LSTM) time series learning model. By analyzing historical stock price data, the project aims to provide accurate predictions of future stock trends, enabling data-driven investment decisions and risk Jan 3, 2025 · Stock price prediction is a typical complex time series prediction problem characterized by dynamics, nonlinearity, and complexity. This paper introduces a generative adversarial network model that incorporates an attention mechanism (GAN-LSTM-Attention) to improve the accuracy of stock price prediction. Oct 1, 2023 · Long Short-Term Memory (LSTM) is a type of artificial neural network that is often used in time series analysis. This model Nov 30, 2019 · The associated network model can predict the opening price, the lowest price and the highest price of a stock simultaneously. springer. (2015) LSTM Neural Network with Emotional Analysis for Prediction of Stock Price. The associated network model was compared with LSTM network model and deep recurrent neural network model. Y. To address this, we propose the DCA-BiLSTM model, which combines dual-path convolutional neural networks with an attention mechanism (DCA) and bidirectional long short-term memory networks (BiLSTM). Zhuge, Q. adyu ogev oojqhq vgfec nqnlh ojlc loyxn fscwtl zuwhe excvjb