Analysis of the relation between Turkish twitter messages and stock market index

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

The increasing popularity of social networks also increased the amount of data collected in these networks. Online social applications, which are also defined as social media, provide ways of accessing large amounts of data about their users over the Internet. Not surprisingly, researchers started to focus on the data extraction process in these networks. In this context, data mining applications and data analysis allow researchers to extract some useful information about the masses and people. In this study, we examine a Turkish twitter data set using data mining techniques. Initially, Turkish tweet dataset is collected and emotional words are determined. An analysis is carried out to see if there is a relation between Turkish tweets and the Turkish stock market index. To the best of our knowledge, this is the first study performed on Turkish tweets and stock market index.

Original languageEnglish
Title of host publication2012 6th International Conference on Application of Information and Communication Technologies, AICT 2012 - Proceedings
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 6th International Conference on Application of Information and Communication Technologies, AICT 2012 - Tbilisi, Georgia
Duration: 17 Oct 201219 Oct 2012

Publication series

Name2012 6th International Conference on Application of Information and Communication Technologies, AICT 2012 - Proceedings

Conference

Conference2012 6th International Conference on Application of Information and Communication Technologies, AICT 2012
Country/TerritoryGeorgia
CityTbilisi
Period17/10/1219/10/12

Keywords

  • Data mining
  • social networks
  • stock market
  • twitter

Fingerprint

Dive into the research topics of 'Analysis of the relation between Turkish twitter messages and stock market index'. Together they form a unique fingerprint.

Cite this