Commonsense knowledge-aided news analysis for stock market surveillance

Xin Li; Kun Chen; Terrance Fung; Sherry X. Sun; Huaiqing Wang

Commonsense knowledge-aided news analysis for stock market surveillance

Xin Li
, Kun Chen
, Terrance Fung
, Sherry X. Sun
, Huaiqing Wang

Department of Information Systems

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Stock market surveillance is critical to maintain market fairness and uphold investors' confidence. This research takes a text mining approach to inspect news to help surveillance specialists investigate suspicious stock transactions. Noticing the important role of prior knowledge in humans' news comprehension, we propose to incorporate commonsense knowledge into this task through a graph model. Experiments on a dataset collected from the Hong Kong stock market show that commonsense knowledge, especially features extracted from inter-news commonsense relations, can significantly improve market surveillance performance.

Original language	English
Title of host publication	Proceedings of 20th Annual Workshop on Information Technologies and Systems
Publisher	Social Science Research Network
Publication status	Published - Dec 2010
Event	20th Annual Workshop on Information Technologies and Systems, WITS 2010 - St. Louis, MO, United States Duration: 11 Dec 2010 → 12 Dec 2010

Conference

Conference	20th Annual Workshop on Information Technologies and Systems, WITS 2010
Place	United States
City	St. Louis, MO
Period	11/12/10 → 12/12/10

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@inproceedings{1fe538b3e74048f885844bc67ded53c0,

title = "Commonsense knowledge-aided news analysis for stock market surveillance",

abstract = "Stock market surveillance is critical to maintain market fairness and uphold investors' confidence. This research takes a text mining approach to inspect news to help surveillance specialists investigate suspicious stock transactions. Noticing the important role of prior knowledge in humans' news comprehension, we propose to incorporate commonsense knowledge into this task through a graph model. Experiments on a dataset collected from the Hong Kong stock market show that commonsense knowledge, especially features extracted from inter-news commonsense relations, can significantly improve market surveillance performance.",

author = "Xin Li and Kun Chen and Terrance Fung and Sun, \{Sherry X.\} and Huaiqing Wang",

year = "2010",

month = dec,

language = "English",

booktitle = "Proceedings of 20th Annual Workshop on Information Technologies and Systems",

publisher = "Social Science Research Network",

note = "20th Annual Workshop on Information Technologies and Systems, WITS 2010 ; Conference date: 11-12-2010 Through 12-12-2010",

}

Commonsense knowledge-aided news analysis for stock market surveillance. / Li, Xin; Chen, Kun; Fung, Terrance et al.
Proceedings of 20th Annual Workshop on Information Technologies and Systems. Social Science Research Network, 2010.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Commonsense knowledge-aided news analysis for stock market surveillance

AU - Li, Xin

AU - Chen, Kun

AU - Fung, Terrance

AU - Sun, Sherry X.

AU - Wang, Huaiqing

PY - 2010/12

Y1 - 2010/12

N2 - Stock market surveillance is critical to maintain market fairness and uphold investors' confidence. This research takes a text mining approach to inspect news to help surveillance specialists investigate suspicious stock transactions. Noticing the important role of prior knowledge in humans' news comprehension, we propose to incorporate commonsense knowledge into this task through a graph model. Experiments on a dataset collected from the Hong Kong stock market show that commonsense knowledge, especially features extracted from inter-news commonsense relations, can significantly improve market surveillance performance.

AB - Stock market surveillance is critical to maintain market fairness and uphold investors' confidence. This research takes a text mining approach to inspect news to help surveillance specialists investigate suspicious stock transactions. Noticing the important role of prior knowledge in humans' news comprehension, we propose to incorporate commonsense knowledge into this task through a graph model. Experiments on a dataset collected from the Hong Kong stock market show that commonsense knowledge, especially features extracted from inter-news commonsense relations, can significantly improve market surveillance performance.

UR - https://www.scopus.com/pages/publications/84900462364

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84900462364&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

BT - Proceedings of 20th Annual Workshop on Information Technologies and Systems

PB - Social Science Research Network

T2 - 20th Annual Workshop on Information Technologies and Systems, WITS 2010

Y2 - 11 December 2010 through 12 December 2010

ER -

Commonsense knowledge-aided news analysis for stock market surveillance

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