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.

中文翻译：

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使用基于中智逻辑的情绪分析增强对股市走势的预测

社交媒体平台使许多人能够公开表达和传播他们的意见。研究人员非常感兴趣的一个话题是社交媒体对预测股市的影响。关于公司或服务的正面或负面反馈可能会影响其股价。然而，利用情绪分析（SA）预测股市走势遇到了障碍，因为之前的研究表明情绪分析技术存在不精确性，忽视了数据固有的不确定性，从而直接损害了股市指标的可信度。在本文中，我们提出了一种新的模型，通过使用中智逻辑（NL）改进 SA 过程来增强 SA 对股市走势的预测，该模型通过处理不确定和不确定的数据来准确地对推文进行分类。对于预测模型，我们使用情绪分析结果和历史股市数据作为称为长短期记忆（LSTM）的深度学习算法的输入，以预测特定天数后的股票走势。这项研究的结果表明，使用相同数据集预测股票价格波动的预测准确性超过了先前研究的准确性。