Quantifying StockTwits Semantic Terms' Trading Behavior in Financial Markets: An Effective Application of Decision Tree Algorithms
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 020-112245-5379
- Type
- D - Journal article
- DOI
-
10.1016/j.eswa.2015.08.008
- Title of journal
- Expert Systems With Applications
- Article number
- -
- First page
- 9192
- Volume
- 42
- Issue
- 23
- ISSN
- 1873-6793
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2015
- URL
-
https://www.sciencedirect.com/science/article/pii/S0957417415005473
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is published in a journal that is ranked 2nd out of 83 journals in Operations Research and among top 15% in Computer Science: Artificial Intelligence according to Web of Science. The work introduces a new way to predict sentiment (bullish vs bearish) on stock trading based upon tweet data. It is a substantial extension of a paper at Discovery Science 2013 part of the Springer Verlag Lecture Notes in Computer Science series.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
