article · Journal of theoretical and applied electronic commerce research
Public opinion on social media can influence company share valuations, making sentiment analysis a valuable tool for financial forecasting. However, conventional sentiment analysis methods often struggle with imprecision because they fail to account for uncertain and indeterminate information within public posts, weakening the credibility of market indicators. To resolve this issue, an alternative framework applies neutrosophic logic to classify tweets, specifically addressing ambiguous data. The resulting sentiment measurements are combined with historical market records and supplied to a long short-term memory deep learning network to forecast stock movements across a set number of days. Testing on established benchmark data demonstrated that this approach achieves higher predictive accuracy for stock price fluctuations than previous models evaluated on the same dataset.
Financial markets react rapidly to public mood, but online discourse is full of ambiguity that standard analytics tools misinterpret. By using neutrosophic logic to account for uncertain expressions, predictive systems can extract more dependable signals from social media. This leads to more reliable market forecasting models, helping observers better understand the relationship between digital public sentiment and real-world asset price movements.
The model could support algorithmic trading platforms, quantitative fund managers, and financial analytics providers wishing to enhance sentiment-driven decision tools. The research represents an applied and tested stage, having demonstrated success on historical benchmark datasets. Real-world adoption would depend on further development to handle real-time streaming data within live trading systems.
AI-generated from the published abstract. Always read the original work before citing.
Social media platforms have allowed many people to publicly express and disseminate their opinions. A topic of considerable interest among researchers is the impact of social media on predicting the stock market. Positive or negative feedback about a company or service can potentially impact its stock price. Nevertheless, the prediction of stock market movement using sentiment analysis (SA) encounters hurdles stemming from the imprecisions observed in SA techniques demonstrated in prior studies, which overlook the uncertainty inherent in the data and consequently directly undermine the credibility of stock market indicators. In this paper, we proposed a novel model to enhance the prediction of stock market movements using SA by improving the process of SA using neutrosophic logic (NL), which accurately classifies tweets by handling uncertain and indeterminate data. For the prediction model, we use the result of sentiment analysis and historical stock market data as input for a deep learning algorithm called long short-term memory (LSTM) to predict the stock movement after a specific number of days. The results of this study demonstrated a predictive accuracy that surpasses the accuracy rate of previous studies in predicting stock price fluctuations when using the same dataset.
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DOI: 10.3390/jtaer19010007
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