Sentiment Aware Stock Price Forecasting using an SA-RNN-LBL Learning Model

Abstract

Stock market historical information is often utilized in technical analyses for identifying and evaluating patterns that could be utilized to achieve profits in trading. Although technical analysis utilizing various measures has been proven to be helpful for forecasting and predicting price trends, its utilization in formulating trading orders and rules in an automated system is complex due to the indeterminate nature of the rules. Moreover, it is hard to define a specific combination of technical measures that identify better trading rules and points, since stocks might be affected by different external factors. Thus, it is important to incorporate investors’ sentiments in forecasting operations, considering dynamically the varying stock behavior. This paper presents a sentiment aware stock forecasting model using a Log BiLinear (LBL) model for learning short term stock market sentiment patterns, and a Recurrent Neural Network (RNN) for learning long-term stock market sentiment patterns. The Sentiment Aware Stock Price Forecasting (SASPF) model achieves a much superior performance compared to standard deep learning based stock price forecasting models.

Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework

“…Sentiment analysis processes and identifies the data of certain domains through natural language processing. Sentiment analysis using social media plays a crucial role in various fields such as social development, people's awareness, economic development [10][11][12]. Indeed, many studies applied ML, and Deep Learning (DL) methods and algorithms to explore and investigate the public's sentiments on social media platforms towards the outbreak of COVID-19 and its emerging variants [13][14][15].…”

Omicron virus emotions understanding system based on deep learning architecture

“…Sentiment analysis has acquired a significant interest in the last few years because it has introduced remarkable services in many aspects regarding public opinion mining and recognition in the field of marketing, seeking product reviews, reviews of movies, and healthcare issues based on sentiment understanding. This conducted research has utilized the issue of Omicron COVID-19 virus as a case study to implement a sentiment analysis framework to explore the global attitude and sentiment toward Omicron variant (Madhukar and Verma 2017 ; Gurav and Kotrappa 2020 ; Liu et al 2020 ; Zucco et al 2020 ). Many studies regarding COVID-19 virus sentiment analysis have been proposed lately (Garcia and Berton 2021 ; De Melo and Figueiredo 2021 ; Nemes and Kiss 2021 ; Basiri et al 2021 ; Khan et al 2020 ; Almotiri 2022 ; Avasthi et al 2022 ; Mohamed et al 2022 ).…”

Evaluation of Stock Closing Prices using Transformer Learning

“…The LSTM model outperformed GRU with a lower RMSE of 4.7524 and an MAE of 2.4377. In [26], a novel model was introduced combining LBL and RNN to capture short-and long-term sentiment patterns.…”