论文研究 - 基于股票时间序列数据和网络舆情文本数据的股市热点事件检测

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随着Internet世界与现实世界的高度集成，Internet信息不仅为金融投资者提供实时有效的数据，而且还帮助他们了解市场动态，并使投资者能够快速识别可能导致股票下跌的相关金融事件。市场波动。但是，在金融领域的事件检测研究中，许多研究集中在微博客，新闻和其他网络文本信息上。很少有学者研究金融时间序列数据的特征。考虑到在金融领域，事件的发生经常影响在线舆论空间和真实交易空间，因此本文提出了一种基于股票交易时间序列数据和在线舆论文本的多源异构信息检测方法。数据以检测股市中的热点事件。该方法采用离群检测算法，基于多成员融合提取股市热点事件的时间。并根据本文提出的特征项权重计算公式，计算网络舆情信息的关键词权重，得出股市热点事件的核心内容。最后，实现了对股票市场热点事件的准确检测。

Journal of Data Analysis and Information Processing, 2019, 7, 174-189

https://www.scirp.org/journal/jdaip

ISSN Online: 2327-7203

ISSN Print: 2327-7211

DOI:

10.4236/jdaip.2019.74011 Sep. 30, 2019 174 Journal of Data Analysis and Information Processing

Hot Events Detection of Stock Market Based on

Time Series Data of Stock and Text Data of

Network Public Opinion

Beibei Cao

Department of Publishing and Dissemination, Shanghai Publishing and Printing College, Shanghai, China

Abstract

With the highly integration of the

Internet world and the real world, Internet

information not only provides real-time and effective data for financial in-

vestors, but also helps them understand market dynamics, and enables inves-

tors to quickly identify relevant financial events that may lea

d to stock market

volatility. However, in the research of event detection in the financial field,

many studies are focused on micro-blog, news and other network text infor-

mation. Few scholars have studied the characteristics of financial time series

data. Considering that in the financial field, the occurrence of an event often

affects both the online public opinion space and the real transaction space, so

this paper proposes a multi-source heterogeneous information detection me-

thod based on stock transacti

on time series data and online public opinion

text data to detect hot events in the stock market. This method uses outlier

detection algorithm to extract the time of hot events in stock market based on

multi-member fusion. And according to the weight calcu

lation formula of the

feature item proposed in this paper, this method calculates the keyword

weight of network public opinion information to obtain the core content of

hot events in the stock market. Finally, accurate detection of stock market hot

events is achieved.

Keywords

Relationship, Network Public Opinion, Stock Trading Behavior, Stock Market

Hot Events

1. Introduction

In the securities industry, once market fluctuations occur, investors first hope to

find the answer from the Internet information. However, the geometric expan-

How to cite this paper:

Cao, B.B. (2019

)

Hot Events Detection of Stock Market

Based on Time Series Data of Stock and

Text Data of Network Public Opinion.

Journal of Data Analysis and Information

Processing

, 174-189.

https://doi.org/10.4236/jdaip.2019.74011

Received:

July 11, 2019

Accepted:

September 27, 2019

Published:

September 30, 2019

9 by author(s) and

Scientific

Research Publishing Inc.

This work is licensed under the Creative

Commons Attribution International

License (CC BY

4.0).

http://creativecommons.org/licenses/by/4.0/

Open Access

B. B. Cao

DOI:

10.4236/jdaip.2019.74011 175 Journal of Data Analysis and Information Processing

sion of Internet information makes it more and more difficult for people to ex-

tract effective information. If investors are unable to obtain timely and accurate

information about events that lead to financial market volatility, then the losses

caused are incalculable. Therefore, how to quickly find valuable topics and

events from a large number of Internet data is particularly important.

With time goes by, numerous research methods for event discovery have been

put forward [1]-[11]. However, most of these methods are based on text data

[1]-[11] or time series data [12]-[19] for event discovery separately. There are

few scholars, to the best of our knowledge, study the characteristics of financial

time series data and text data to carry out research [20] [21] [22]. As a realistic

behavior of financial markets, time-series data such as stock trading data and

market data are often affected by events and can better reflect changes before

and after events. Therefore, this paper studies the discovery of financial

events by combining network text information and financial time series data, so

as to help investors to quickly obtain hot events and correctly grasp market

dynamics.

2. Post’s Influence of Network Public Opinion Space

2.1. Definition and Quantification of Post’s Activity

In web forums, netizens can express their concern for specific information by

posting, reading and replying. And this degree of attention is an important ex-

ternal feature of the emotional tendency of network public opinion. In this pa-

per, we call it post’s activity. In order to quantify the user’s attention to topic in-

formation intuitively, we calculate it by the amount of readings and the amount

of comments of the posts. Among them, the readings amount of posts reflects

the degree of dissemination of the information contained in the posts and it is

the instinct concern of users. The comments amount of posts reflects the atten-

tion paid to the information contained in the posts. And it is the manifestation

of the user’s emphasis on topic interaction, and its emotional intensity is strong-

er. So in this paper, we choose the amount of readings and the amount of com-

ments as indicators of post’s activity. The specific definitions are as follows:

Definition 2-1 Post’s activity: Assuming that within a period of time

, a total

posts are posted in the online public opinion space, which are

{

}

,,,,,

pp p p

. The readings amount of the

-th post is

, and the

comments amount is

. Then the total readings amount of

posts in the

time period

r pr

∑

, and the average readings amount of each post is

avg r

. We define the propagation coefficient

as the ratio of

the readings amount of

in time period

to the average reading amount of

each post in the same time period. The formula of

avg r

. Si-

milarly, the attention coefficient

is defined as the ratio of the

comment amount of

in time period

to the average comment amount of

B. B. Cao

DOI:

10.4236/jdaip.2019.74011 176 Journal of Data Analysis and Information Processing

each post in the same time period, and the formula is

avg c

, where

avg c

= =

∑

. Finally, the activity of

is defined as the sum of its

propagation coefficient and the attention coefficient, namely:

_ __

i ii

pAc pp pc

pr pc

avg r avg c

pr pc

= =

= +

∑∑

(1)

2.2. Definition and Quantification of User’s Influence

The influence of users in the stock bar forum refers to the popularity index of the

user in the stock bar. It is mainly affected by the age of the user, the amount of

comments posted by the user, the amount of forwarding, and other factors. So in

this paper we use user’s power, user’s activity and user’s attention to measure us-

er’s influence in the stock bar forum.

2.2.1. User’s Power

User’s power is the potential energy that users have under static conditions. It is

mainly reflected in the three factors of age, the amount of fans and the amount of

people that user concern.

Definition 2-2 User’s power: The user’s power of the

-th user

is defined as

ii i

Pa Pfr Pfe

, where

is the age of the user

is the average

age of all users,

Pfr

is the amount of fans of the user

Pfr

is the average

amount of fans of all users,

Pfe

is the amount of people that user

concern,

and

Pfe

is the average amount of people that all users concern.

2.2.2. User’s Activity

User’s activity reflects the degree of user’s autonomy, which is mainly determined

by the amount of postings and the amount of comments. A new post usually

contains a new topic. Therefore, the more posts a user publishes, the easier it is

for other users to pay attention to the post, and the greater the influence of the

user. Comments reflect the user’s views and opinions about other people’s infor-

mation, and it is also a manifestation of user’s activity.

Definition 2-3 User’s activity: The user’s activity of the

-th user

is defined

Pp Pd

a Ac

, where

represents the total number of posts for the

user

represents the average number of posts for all users,

represents the total number of comments for the user, and

represents the

B. B. Cao

DOI:

10.4236/jdaip.2019.74011 177 Journal of Data Analysis and Information Processing

average number of comments for all users.

2.2.3. User’s Attention

User’s attention mainly reflects the degree of attraction and attention of users to

other users in the online forum. When a user’s posts are commented by a large

number of other users, it shows that the quality of these posts are high and at-

tractive, which further indicates that the user has great influence. In addition,

there are some users who are not good at commenting, but are used to express-

ing their concern about posts through reading, which also shows the attraction

of posts to them. Therefore, the total amount of readings also needs to be re-

garded as the influencing factor of users’ attention.

Definition 2-4 User’s attention: The user’s attention of the

-th user

defined as

Pr Pc

a At

, where

represents the average readings amount

of all posts of user

represents the average readings amount of all users’

posts,

represents the average comments amount of all posts of user

, and

represents the average comments amount of all users’ posts.

Based on the above three user indicators, the paper calculates the user’s influ-

ence formula as follows:

___ _

iii i

aI aP aAc aAt=++

In the end, we calculate the post’s influence of network public opinion space

by combining the activity of the post and the influence of the poster. The calcu-

lation formula is as follows:

_ 0.7 _ 0.3 _

ii i

pI pAc aI=× +×

(2)

3. Hot Event Detection of Stock Market

3.1. Definition of Stock Market Hot Events

The events to be studied in this paper refer to hot events that are related to the

stock market and can lead to changes in stock trading behavior. This paper de-

fines it as hot events of stock market. It is embodied in the following three cha-

racteristics:

1) The events corresponding to popular posts (which have been read and

commented for many times and have high influence) on the web forums.

2) The events corresponding to online hot news (which is reported or repro-

duced by multiple news websites).

3) The events that can have a significant impact on the stock market.

The first two are based on the feedback from the online public opinion space

to understand the stock market hot events, and the third is to understand the hot

events of stock market based on the information fed back from the real trading

conditions of the stock market. These three event characteristics will be fully

combined below, and on this basis, we will conduct a research on the detection of

hot events in the stock market.

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