  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%.
