Version 1
: Received: 7 April 2024 / Approved: 8 April 2024 / Online: 8 April 2024 (15:45:25 CEST)
How to cite:
Smatov, N.; Kalashnikov, R.; Kartbayev, A. Development of Context-based Sentiment Classification for Intelligent Stock Market Prediction. Preprints2024, 2024040563. https://doi.org/10.20944/preprints202404.0563.v1
Smatov, N.; Kalashnikov, R.; Kartbayev, A. Development of Context-based Sentiment Classification for Intelligent Stock Market Prediction. Preprints 2024, 2024040563. https://doi.org/10.20944/preprints202404.0563.v1
Smatov, N.; Kalashnikov, R.; Kartbayev, A. Development of Context-based Sentiment Classification for Intelligent Stock Market Prediction. Preprints2024, 2024040563. https://doi.org/10.20944/preprints202404.0563.v1
APA Style
Smatov, N., Kalashnikov, R., & Kartbayev, A. (2024). Development of Context-based Sentiment Classification for Intelligent Stock Market Prediction. Preprints. https://doi.org/10.20944/preprints202404.0563.v1
Chicago/Turabian Style
Smatov, N., Ruslan Kalashnikov and Amandyk Kartbayev. 2024 "Development of Context-based Sentiment Classification for Intelligent Stock Market Prediction" Preprints. https://doi.org/10.20944/preprints202404.0563.v1
Abstract
This paper presents a novel approach to sentiment analysis specifically customized for predicting stock market movements, bypassing the need for external dictionaries which are often unavailable for many languages. Our methodology directly analyzes textual data, with a particular focus on context-specific sentiment words within neural network models. This specificity ensures our sentiment analysis is both relevant and accurate in identifying trends in the stock market. We employ sophisticated mathematical modeling techniques to enhance both the precision and interpretability of our models. Through meticulous data handling and advanced machine learning methods, we leverage large datasets from Twitter and financial markets to examine the impact of social media sentiment on financial trends. We achieved an accuracy exceeding 75%, highlighting the effectiveness of our modeling approach, which we further refined into a convolutional neural network model. This achievement contributes valuable insights into sentiment analysis within the financial domain, thereby improving the overall clarity of forecasting in this field.
Keywords
sentiment analysis; neural networks; stock price prediction; text-mining; deep learning.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
