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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2018

21.11.2016 | Original Article

Investor sentiment identification based on the universum SVM

verfasst von: Wen Long, Ye-ran Tang, Ying-jie Tian

Erschienen in: Neural Computing and Applications | Ausgabe 2/2018

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Abstract

Universum refers to additional samples which contain priori knowledge for classification but belonging to none of the class. It has been proved that universum positioned “in between” the two classes obtain better results. Since opinions on stock market defined as investor sentiment involve quite a number of neutral views, these neutral views can be used as universum samples to better identify investor sentiment. With universum samples, this paper uses support vector machine (SVM) to classify posts on stock forum. We define bullish views as positive samples, define bearish views as negative samples, and also further discuss the situation of a 3-class problem with neutral views. Compared with standard SVM, the empirical studies with universum samples in this paper show better performance for both 2- and 3-class classifications.
Vorheriger Artikel CRHS: clustering and routing in wireless sensor networks using harmony search algorithm
Nächster Artikel An improved ranking method for comparing trapezoidal intuitionistic fuzzy numbers and its applications to multicriteria decision making

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Metadaten
Titel
Investor sentiment identification based on the universum SVM
verfasst von
Wen Long
Ye-ran Tang
Ying-jie Tian
Publikationsdatum
21.11.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2684-y

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