Text Classification Using Time Windows Applied to Stock Exchange

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43912417" target="_blank" >RIV/62156489:43110/17:43912417 - isvavai.cz</a>

Výsledek na webu

<a href="http://sdiwc.net/digital-library/text-classification-using-time-windows-applied-to-stock-exchangern" target="_blank" >http://sdiwc.net/digital-library/text-classification-using-time-windows-applied-to-stock-exchangern</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Text Classification Using Time Windows Applied to Stock Exchange

Popis výsledku v původním jazyce

Each day, a lot of text data is generated. This data comes from various sources and may contain valuable information. In this article, we use text classification to discover if there is a connection between textual documents (specifically Facebook posts) and changes of the S&amp;P 500 stock index. The index values and documents were divided into time windows according to the direction of the index value changes. In the first experiment, we used a batch processing approach to put the documents from all windows into one data set and a classification accuracy of 62% was achieved. In the second experiment, we used a data stream approach to divide documents into twelve data sets created from two neighboring windows and we achieved an accuracy of 68%. This indicates that posts, which companies write on their Facebook pages, are partially related to the performance of the stock index. Taking the concept change into account also enables better quantification of this relationship.

Název v anglickém jazyce

Text Classification Using Time Windows Applied to Stock Exchange

Popis výsledku anglicky

Each day, a lot of text data is generated. This data comes from various sources and may contain valuable information. In this article, we use text classification to discover if there is a connection between textual documents (specifically Facebook posts) and changes of the S&amp;P 500 stock index. The index values and documents were divided into time windows according to the direction of the index value changes. In the first experiment, we used a batch processing approach to put the documents from all windows into one data set and a classification accuracy of 62% was achieved. In the second experiment, we used a data stream approach to divide documents into twelve data sets created from two neighboring windows and we achieved an accuracy of 68%. This indicates that posts, which companies write on their Facebook pages, are partially related to the performance of the stock index. Taking the concept change into account also enables better quantification of this relationship.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA16-26353S" target="_blank" >GA16-26353S: Sentiment a jeho vliv na akciové trhy</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of New Computer Architectures and Their Applications

ISSN

2412-3587

e-ISSN

—

Svazek periodika

7

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CN - Čínská lidová republika

Počet stran výsledku

6

Strana od-do

62-67

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—