Hybrid Deep Learning and Sentiment Analysis Framework for High-Frequency Stock Market Prediction Using Social Media Data.

Sofia Arkhstan

Hybrid Deep Learning and Sentiment Analysis Framework for High-Frequency Stock Market Prediction Using Social Media Data.

Sofia Arkhstan
Volume 5

Abstract

High-frequency stock market prediction is a challenging financial forecasting task due to the nonlinear, noisy, and rapidly changing nature of intraday price movements. Traditional forecasting models often rely primarily on historical market indicators, such as price, volume, volatility, bid–ask spread, and technical signals, while neglecting the growing influence of investor sentiment expressed through social media platforms. In modern financial markets, social media posts, financial news discussions, online investor communities, and real-time public opinions can affect short-term trading behavior, market liquidity, volatility spikes, and price momentum. However, extracting meaningful predictive information from social media is difficult because of informal language, sarcasm, misinformation, noisy text, temporal misalignment, and the high velocity of data streams. To address these challenges, this paper proposes a hybrid deep learning and sentiment analysis framework for high-frequency stock market prediction using social media data.

The proposed framework integrates market microstructure data with sentiment-enriched textual features to improve short-term forecasting accuracy. Historical intraday stock data, including open, high, low, close prices, trading volume, returns, volatility, moving averages, order book indicators, and technical momentum signals, are combined with social media-derived features extracted from platforms such as financial forums, microblogs, and investor discussion networks. A comprehensive preprocessing pipeline is applied to clean textual data, remove spam and irrelevant posts, normalize financial slang, identify stock-related entities, handle emojis and hashtags, and align sentiment features with high-frequency market intervals. Sentiment analysis is performed using lexicon-based methods, transformer-based language models, and domain-specific financial NLP models to estimate positive, negative, neutral, and intensity-weighted investor sentiment.

The predictive architecture combines deep learning models for both numerical and textual data streams. Convolutional neural networks and transformer encoders are used to extract semantic representations from social media text, while LSTM, GRU, temporal convolutional networks, and attention-based models are employed to capture sequential dependencies in high-frequency market data. The extracted sentiment embeddings and market features are fused using a hybrid feature-level or decision-level fusion strategy to predict short-term stock price direction, return movement, volatility change, or trading signal classification. Attention mechanisms are incorporated to dynamically weight the most relevant time windows, sentiment events, and market indicators, allowing the model to focus on periods where investor sentiment has stronger predictive influence.

The proposed framework can be evaluated using high-frequency stock market datasets combined with timestamped social media streams over multiple trading periods. Model performance is assessed using accuracy, precision, recall, F1-score, AUC-ROC, mean absolute error, root mean square error, directional accuracy, Sharpe ratio, maximum drawdown, and simulated trading profitability. Comparative analysis is conducted against traditional statistical models, technical indicator-based machine learning methods, standalone sentiment models, LSTM-only models, transformer-only models, and non-hybrid forecasting baselines. Experimental evaluation is expected to demonstrate that integrating sentiment signals with deep temporal market modeling improves prediction robustness, particularly during periods of high market uncertainty, news-driven volatility, and abnormal investor attention.

The proposed system provides a scalable and adaptive framework for high-frequency financial forecasting by combining social media sentiment mining with deep learning-based market prediction. By transforming noisy public opinion streams into structured predictive signals, the framework can support algorithmic trading, risk monitoring, volatility forecasting, portfolio adjustment, and real-time decision support. Its ability to jointly model textual sentiment, temporal market behavior, and attention-driven feature interactions makes it a promising approach for next-generation intelligent financial analytics systems.

Keywords

References

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