A novel Twitter sentiment analysis model with baseline correlation for financial market prediction with improved efficiency
File(s)
Accepted version
Author(s)
Guo, Xinyi
Li, Jinfeng
Type
Conference Paper
Abstract
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%.
Date Issued
2019-12-16
Date Acceptance
2019-12-01
Citation
2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), 2019
Publisher
IEEE
Journal / Book Title
2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)
Copyright Statement
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/8931720
Source
2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)
Subjects
cs.SI
cs.SI
cs.LG
q-fin.ST
Publication Status
Published
Start Date
2019-10-22
Finish Date
2019-10-25
Coverage Spatial
Granada, Spain
Date Publish Online
2019-12-16