论文标题
一种具有基线相关性的新型Twitter情感分析模型，用于金融市场预测，并提高效率
A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency
论文作者
论文摘要
与基于封闭式基金折扣（CEFD）的常规计量经济学模型相比，基于Twitter情绪评分（TSS）提出了一种新颖的社交网络情感分析模型，以实时预测未来股票市场价格FTSE 100的实时预测。拟议的TSS模型采用了一种新的基线相关方法，该方法不仅表现出不错的预测准确性，而且还减轻了计算负担，并在没有历史数据的情况下实现了快速的决策。多项式回归，分类建模和基于词典的情感分析是使用R进行的。所获得的TSS预测未来的股票市场趋势会提前15个时间样本（30个工作小时），精度为67.22％，使用拟议的基线标准，而无需参考历史TSS或市场数据。具体而言，发现TSS对向上市场的预测性能远胜于下降市场。在逻辑回归和线性判别分析下，TSS在预测未来市场的上升趋势方面的准确性达到97.87％。
A novel social networks sentiment analysis model is proposed based on Twitter sentiment score (TSS) for real-time prediction of the future stock market price FTSE 100, as compared with conventional econometric models of investor sentiment based on closed-end fund discount (CEFD). The proposed TSS model features a new baseline correlation approach, which not only exhibits a decent prediction accuracy, but also reduces the computation burden and enables a fast decision making without the knowledge of historical data. Polynomial regression, classification modelling and lexicon-based sentiment analysis are performed using R. The obtained TSS predicts the future stock market trend in advance by 15 time samples (30 working hours) with an accuracy of 67.22% using the proposed baseline criterion without referring to historical TSS or market data. Specifically, TSS's prediction performance of an upward market is found far better than that of a downward market. Under the logistic regression and linear discriminant analysis, the accuracy of TSS in predicting the upward trend of the future market achieves 97.87%.
