A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency

Xinyi Guo, Jinfeng Li*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

59 Citations (Scopus)

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%

Original languageEnglish
Title of host publication2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019
EditorsMohammad Alsmirat, Yaser Jararweh
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages472-477
Number of pages6
ISBN (Electronic)9781728129464
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019 - Granada, Spain
Duration: 22 Oct 201925 Oct 2019

Publication series

Name2019 6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019

Conference

Conference6th International Conference on Social Networks Analysis, Management and Security, SNAMS 2019
Country/TerritorySpain
CityGranada
Period22/10/1925/10/19

Keywords

  • big data analytics
  • closed-end fund discounts
  • financial prediction
  • lexicon-based classification
  • Twitter sentiment

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