An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery

Ning Cao; Yansong Wang; Xiaoyu Chen; Yulan Zhou; Mingrui Wu; Xiaofang Li; Jianrui Ding; Dongjie Zhu

doi:10.1007/978-3-030-78618-2_41

An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery

Ning Cao
, Yansong Wang
, Xiaoyu Chen
, Yulan Zhou
, Mingrui Wu
, Xiaofang Li
, Jianrui Ding
, Dongjie Zhu^*

^*Corresponding author for this work

Sanming University
School of Computer Science and Technology, Harbin Institute of Technology
Harbin Institute of Technology Weihai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In recent years, the emergence of a large number of venture firms has brought great profits to venture capital firms. However, it is not easy to identify venture firms with investment prospects. Therefore, based on frequent item sets, this paper mainly mines the enterprise text information to identify the venture enterprises with investment prospects. Firstly, we use TF-IDF algorithm to extract keywords from enterprise text; Secondly, the word2VEC model is used to vectorize the text keywords, and cosine similarity is calculated with the word vectors in the keyword database; Finally, we use the Apriori algorithm to find frequent item sets and generate association rules, complete vector weighting calculation of combination keywords, and finally retain the first three words or phrases with the highest weight as the identification keywords of the enterprise, thus determining whether the enterprise is a risk company with potential investment prospects. Experimental results show that the proposed method is effective.

Original language	English
Title of host publication	Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings
Editors	Xingming Sun, Xiaorui Zhang, Zhihua Xia, Elisa Bertino
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	496-509
Number of pages	14
ISBN (Print)	9783030786175
DOIs	https://doi.org/10.1007/978-3-030-78618-2_41
State	Published - 2021
Externally published	Yes
Event	7th International Conference on Artificial Intelligence and Security, ICAIS 2021 - Dublin, Ireland Duration: 19 Jul 2021 → 23 Jul 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1423
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	7th International Conference on Artificial Intelligence and Security, ICAIS 2021
Country/Territory	Ireland
City	Dublin
Period	19/07/21 → 23/07/21

Keywords

Cosine similarity
Discovery frequency term
TF-IDF
Venture firms identification
Word2vec

Access to Document

10.1007/978-3-030-78618-2_41

Cite this

Cao, N., Wang, Y., Chen, X., Zhou, Y., Wu, M., Li, X., Ding, J., & Zhu, D. (2021). An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery. In X. Sun, X. Zhang, Z. Xia, & E. Bertino (Eds.), Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings (pp. 496-509). (Communications in Computer and Information Science; Vol. 1423). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-78618-2_41

Cao, Ning ; Wang, Yansong ; Chen, Xiaoyu et al. / An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery. Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings. editor / Xingming Sun ; Xiaorui Zhang ; Zhihua Xia ; Elisa Bertino. Springer Science and Business Media Deutschland GmbH, 2021. pp. 496-509 (Communications in Computer and Information Science).

@inproceedings{69366fd1175d414888c64d07ecd030e6,

title = "An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery",

abstract = "In recent years, the emergence of a large number of venture firms has brought great profits to venture capital firms. However, it is not easy to identify venture firms with investment prospects. Therefore, based on frequent item sets, this paper mainly mines the enterprise text information to identify the venture enterprises with investment prospects. Firstly, we use TF-IDF algorithm to extract keywords from enterprise text; Secondly, the word2VEC model is used to vectorize the text keywords, and cosine similarity is calculated with the word vectors in the keyword database; Finally, we use the Apriori algorithm to find frequent item sets and generate association rules, complete vector weighting calculation of combination keywords, and finally retain the first three words or phrases with the highest weight as the identification keywords of the enterprise, thus determining whether the enterprise is a risk company with potential investment prospects. Experimental results show that the proposed method is effective.",

keywords = "Cosine similarity, Discovery frequency term, TF-IDF, Venture firms identification, Word2vec",

author = "Ning Cao and Yansong Wang and Xiaoyu Chen and Yulan Zhou and Mingrui Wu and Xiaofang Li and Jianrui Ding and Dongjie Zhu",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 7th International Conference on Artificial Intelligence and Security, ICAIS 2021 ; Conference date: 19-07-2021 Through 23-07-2021",

year = "2021",

doi = "10.1007/978-3-030-78618-2\_41",

language = "英语",

isbn = "9783030786175",

series = "Communications in Computer and Information Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "496--509",

editor = "Xingming Sun and Xiaorui Zhang and Zhihua Xia and Elisa Bertino",

booktitle = "Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings",

address = "德国",

}

Cao, N, Wang, Y, Chen, X, Zhou, Y, Wu, M, Li, X, Ding, J & Zhu, D 2021, An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery. in X Sun, X Zhang, Z Xia & E Bertino (eds), Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings. Communications in Computer and Information Science, vol. 1423, Springer Science and Business Media Deutschland GmbH, pp. 496-509, 7th International Conference on Artificial Intelligence and Security, ICAIS 2021, Dublin, Ireland, 19/07/21. https://doi.org/10.1007/978-3-030-78618-2_41

An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery. / Cao, Ning; Wang, Yansong; Chen, Xiaoyu et al.
Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings. ed. / Xingming Sun; Xiaorui Zhang; Zhihua Xia; Elisa Bertino. Springer Science and Business Media Deutschland GmbH, 2021. p. 496-509 (Communications in Computer and Information Science; Vol. 1423).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery

AU - Cao, Ning

AU - Wang, Yansong

AU - Chen, Xiaoyu

AU - Zhou, Yulan

AU - Wu, Mingrui

AU - Li, Xiaofang

AU - Ding, Jianrui

AU - Zhu, Dongjie

PY - 2021

Y1 - 2021

N2 - In recent years, the emergence of a large number of venture firms has brought great profits to venture capital firms. However, it is not easy to identify venture firms with investment prospects. Therefore, based on frequent item sets, this paper mainly mines the enterprise text information to identify the venture enterprises with investment prospects. Firstly, we use TF-IDF algorithm to extract keywords from enterprise text; Secondly, the word2VEC model is used to vectorize the text keywords, and cosine similarity is calculated with the word vectors in the keyword database; Finally, we use the Apriori algorithm to find frequent item sets and generate association rules, complete vector weighting calculation of combination keywords, and finally retain the first three words or phrases with the highest weight as the identification keywords of the enterprise, thus determining whether the enterprise is a risk company with potential investment prospects. Experimental results show that the proposed method is effective.

AB - In recent years, the emergence of a large number of venture firms has brought great profits to venture capital firms. However, it is not easy to identify venture firms with investment prospects. Therefore, based on frequent item sets, this paper mainly mines the enterprise text information to identify the venture enterprises with investment prospects. Firstly, we use TF-IDF algorithm to extract keywords from enterprise text; Secondly, the word2VEC model is used to vectorize the text keywords, and cosine similarity is calculated with the word vectors in the keyword database; Finally, we use the Apriori algorithm to find frequent item sets and generate association rules, complete vector weighting calculation of combination keywords, and finally retain the first three words or phrases with the highest weight as the identification keywords of the enterprise, thus determining whether the enterprise is a risk company with potential investment prospects. Experimental results show that the proposed method is effective.

KW - Cosine similarity

KW - Discovery frequency term

KW - TF-IDF

KW - Venture firms identification

KW - Word2vec

UR - https://www.scopus.com/pages/publications/85111977868

U2 - 10.1007/978-3-030-78618-2_41

DO - 10.1007/978-3-030-78618-2_41

M3 - 会议稿件

AN - SCOPUS:85111977868

SN - 9783030786175

T3 - Communications in Computer and Information Science

SP - 496

EP - 509

BT - Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings

A2 - Sun, Xingming

A2 - Zhang, Xiaorui

A2 - Xia, Zhihua

A2 - Bertino, Elisa

PB - Springer Science and Business Media Deutschland GmbH

T2 - 7th International Conference on Artificial Intelligence and Security, ICAIS 2021

Y2 - 19 July 2021 through 23 July 2021

ER -

Cao N, Wang Y, Chen X, Zhou Y, Wu M, Li X et al. An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery. In Sun X, Zhang X, Xia Z, Bertino E, editors, Advances in Artificial Intelligence and Security - 7th International Conference, ICAIS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 496-509. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-78618-2_41

An Information Identification Method for Venture Firms Based on Frequent Itemset Discovery

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Keywords

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